1 Préparation des données

# Chargement des données 
# Les .csv sont importés et stockés
Donnees_pres <- read.csv("Data/Donnees_pres_agg.csv", header = T, sep = ";", dec = ",", row.names = 2)
Donnees_pres <- as.data.frame(Donnees_pres)
FinJuil_AllPl <- read.csv("Data/FinjuilletAllplants.csv", header = T, sep = ";", dec = ",")
Interactions <- read.csv2("Data/Interactions.csv")
Site_gestion <- read.csv2("Data/Site_Gestion.csv")
Classes <- read.csv2("Data/Classes_poll.csv")
expe_Tonte <- read.csv("Data/Expe_tonte.csv", header = T, sep = ";", dec = ",")

Renommer les colonnes et mise en facteurs des variables

## Données prés
#Site_gestion_date = as.factor(Site_gestion_date),
Donnees_pres <-  Donnees_pres %>% 
  mutate(Site = as.factor(Site),
         Gestion_2 = as.factor(Gestion_2),
         Parcelle = as.factor(Parcelle),
         Gestion_3 = as.factor(Gestion_3),
         Gestion_4 = as.factor(Gestion_4),
         Mixte_isole = as.factor(Mixte_isole),
         Quartier = as.factor(Quartier),
         Jours = as.factor(Jours),
         Gestion_moment_4 = as.factor(Gestion_moment_4),
         Gestion_moment_5 = as.factor(Gestion_moment_5),
         Activite = as.factor(Activite),
         Periode = as.factor(Periode),
         Meteo = as.factor(Meteo))
#colnames(Donnees_pres)

Donnees_pres$Date <- dmy(Donnees_pres$Date)
# Donnees_pres$Heure_debut <- hms(Donnees_pres$Heure_debut)
# Donnees_pres$Heure_fin <- hms(Donnees_pres$Heure_fin)

levels(Donnees_pres$Periode) <- c("Juin", "Mi-juillet", "Fin juillet")
levels(Donnees_pres$Meteo) <- c("Alternances", "Nuageux", "Soleil")
levels(Donnees_pres$Mixte_isole) <- c("Isolé fauche", "Isolé tonte", "Mixte")
levels(Donnees_pres$Gestion_moment_4) <- c("Fauche", "Semé", "Tonte récente", "Tonte tardive")
levels(Donnees_pres$Gestion_moment_5) <- c("Fleuri", "Graminées", "Semé", "Tonte récente", "Tonte tardive")



## Inventaire
#Site_gestion_date,
Inventaire <- Donnees_pres %>% 
  select(Site,
         Nombre_quadrats,
         Gestion_2, Parcelle, Gestion_4,
         Mixte_isole,
         Area_gis_m_sq,
         #Green100, Building100, Impervious100,Natural100
         Quartier,
         Jours,
         Gestion_moment_4, Gestion_moment_5,
         Activite,
         Periode,
         Date, 
         Heure_debut, Heure_fin,
         Temperature, Meteo, 
         Achillea_millefolium:Vicia_tetrasperma_subsp._tetrasperma,
         Aglais_io:Volucella_zonaria)

Inventaire$Gestion_4 <- fct_relevel(Inventaire$Gestion_4, c("Graminees", "Fleuri", "Seme", "Tonte"))
Inventaire$Gestion_moment_5 <- fct_relevel(Inventaire$Gestion_moment_5, c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"))
Inventaire$Mixte_isole <- fct_relevel(Inventaire$Mixte_isole, c("Isolé fauche", "Mixte", "Isolé tonte"))
Inventaire$Activite <- fct_relevel(Inventaire$Activite, c("Nulle", "Moyenne", "Forte"))
Inventaire$Meteo <- fct_relevel(Inventaire$Meteo, c("Nuageux", "Alternances", "Soleil"))


## Fin juillet all plants
FinJuil_AllPl <-  FinJuil_AllPl %>% 
  mutate(Site = as.factor(Site),
         Gestion_2 = as.factor(Gestion_2), 
         Parcelle = as.factor(Parcelle),
         Gestion_4 = as.factor(Gestion_4),
         Mixte_isole = as.factor(Mixte_isole),
         Quartier = as.factor(Quartier),
         Jours = as.factor(Jours),
         Gestion_moment_4 = as.factor(Gestion_moment_4),
         Gestion_moment_5 = as.factor(Gestion_moment_5),
         Activite = as.factor(Activite),
         Periode = as.factor(Periode),
         Meteo = as.factor(Meteo))

FinJuil_AllPl$Date <- dmy(FinJuil_AllPl$Date)
FinJuil_AllPl$Heure_debut <- hms(FinJuil_AllPl$Heure_debut)
FinJuil_AllPl$Heure_fin <- hms(FinJuil_AllPl$Heure_fin)

levels(FinJuil_AllPl$Periode) <- c("Fin juillet")
levels(FinJuil_AllPl$Meteo) <- c("Alternances", "Nuageux", "Soleil")
levels(FinJuil_AllPl$Mixte_isole) <- c("Isolé fauche", "Isolé tonte", "Mixte")
levels(FinJuil_AllPl$Gestion_moment_4) <- c("Fauche", "Semé", "Tonte récente", "Tonte tardive")
levels(FinJuil_AllPl$Gestion_moment_5) <- c("Fleuri", "Graminées", "Semé", "Tonte récente", "Tonte tardive")

AllFinJuillet <- FinJuil_AllPl %>% 
  select(Site,
         nombre_quadrats,
         Gestion_2, Parcelle, Gestion_4,
         Mixte_isole,
         Area,
         #Green100, Building100, Impervious100,Natural100
         Quartier,
         Jours,
         Gestion_moment_4, Gestion_moment_5,
         Activite,
         Periode,
         Date, 
         Heure_debut, Heure_fin,
         Temperature, Meteo, 
         Achillea_millefolium:Vicia_tetrasperma_subsp._tetrasperma,
         Aglais_io:Volucella_zonaria)

AllFinJuillet$Gestion_4 <- fct_relevel(AllFinJuillet$Gestion_4, c("Graminees", "Fleuri", "Seme", "Tonte"))
AllFinJuillet$Gestion_moment_5 <- fct_relevel(AllFinJuillet$Gestion_moment_5, c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"))


## Interactions
Interactions <-  Interactions %>% 
  mutate(Site_gestion_date_Quadrat = as.factor(Site_gestion_date_Quadrat),
         Sp_Plantes = as.factor(Sp_Plantes),
         Sp_Pollinisateurs = as.factor(Sp_Pollinisateurs))



## Site & Classes
colnames(Site_gestion)[1:2] <- c("Site_gestion_date_Quadrat", "Site_gestion_date")
Site_gestion <-  Site_gestion %>% 
  mutate(Site_gestion_date_Quadrat = as.factor(Site_gestion_date_Quadrat),
         Site_gestion_date = as.factor(Site_gestion_date))

colnames(Classes)[1] <- c("Sp_Pollinisateurs")
Classes <-  Classes %>% 
  mutate(Sp_Pollinisateurs = as.factor(Sp_Pollinisateurs) ,
         Classe_Poll = as.factor(Classe_Poll))

2 Illustration et survol des données

2.1 Inventaires plantes et pollinisateurs

Définir une colonne Diversité spécifique (nombre d’espèces, richesse spécifique S) et Abondance des plantes et des pollinisateurs

Inventaire$S_Plant <- specnumber(Inventaire[19:67])
Inventaire$Ab_Plant <- rowSums(Inventaire[19:67])
Inventaire$S_Poll <- specnumber(Inventaire[68:166])
Inventaire$Ab_Poll <- rowSums(Inventaire[68:166])

Jeu de données simplifié et corrélations

Inv <- Inventaire[,c(1:18,167:170)]

corrplot(cor(Inv[,c(7,17,19:22)]), order = "hclust", type = "upper", tl.col = "black")

2.1.1 Statistiques descriptives

2.1.1.1 Graphiques

2.1.1.1.1 Types de gestion
Inv %>% ggplot(aes (x = Gestion_2, y = S_Plant, color = Gestion_2)) + 
  geom_boxplot(alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +
  theme(legend.position = "none") +

Inv %>% ggplot(aes (x = Gestion_2, y = Ab_Plant, color = Gestion_2)) + 
  geom_boxplot(alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Abondance en plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
  theme(legend.position = "none") +

Inv %>% ggplot(aes (x = Gestion_2, y = S_Poll, color = Gestion_2)) + 
  geom_boxplot(alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs") +
  theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Gestion_2, y = Ab_Poll, color = Gestion_2)) + 
  geom_boxplot(alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Abondance en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs") +
  theme(legend.position = "none")

Inv %>% ggplot(aes (x = Gestion_4, y = S_Plant)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +

Inv %>% ggplot(aes (x = Gestion_4, y = Ab_Plant)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
  
Inv %>% ggplot(aes (x = Gestion_4, y = S_Poll)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs") +
  
Inv %>% ggplot(aes (x = Gestion_4, y = Ab_Poll)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Abondance de pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs")

Inv %>% ggplot(aes (x = Gestion_moment_4, y = S_Plant)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_4, y = Ab_Plant)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_4, y = S_Poll)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs", color = "Type de gestion") +
    theme(legend.position="bottom") +
  
Inv %>% ggplot(aes (x = Gestion_moment_4, y = Ab_Poll)) + 
  geom_boxplot(alpha = 0.70) + 
  labs(#title = "Abondance de pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs", color = "Type de gestion") + 
    theme(legend.position="none")

Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Plant)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",#6dcf20
                                  "Tonte tardive" = "#2b790c")) + #12661f
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +
    theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Plant)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
    theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Poll, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) + 
        scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs") +
    theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Poll, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) + 
        scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  labs(#title = "Abondance de pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs") +
    theme(legend.position = "none")

2.1.1.1.2 Selon la période
Inv %>% ggplot(aes (x = Periode, y = S_Plant)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) +
   scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Richesse spécifique en plantes en fonction de la période", 
    x = "", y = "Richesse spécifiqu en plantes") +
  theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Periode, y = Ab_Plant)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
     scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de plantes en fonction de la période",
    x = "", y = "Abondance en plantes") +
  theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Periode, y = S_Poll)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
     scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction de la période",
    x = "Période", y = "Richesse spécifique en pollinisateurs") +
  theme(legend.position = "none") +
  
Inv %>% ggplot(aes (x = Periode, y = Ab_Poll)) +
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
     scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de pollinisateurs en fonction de la période", 
    x = "Période", y = "Abondance en pollinisateurs") +
  theme(legend.position = "none") 

Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Plant)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
       scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +    
  theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Plant, color = Periode)) + 
  geom_boxplot(alpha = 0.70) + 
       scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
  theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Poll, color = Periode)) + 
  geom_boxplot(alpha = 0.70) + 
       scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Poll, color = Periode)) +
  geom_boxplot(alpha = 0.70) + 
       scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs", color = "Période") +
    theme(legend.position="bottom") 

Inv %>% ggplot(aes (x = Periode, y = S_Plant, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) + 
   scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "", y = "Richesse spécifique en plantes") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Periode, y = Ab_Plant, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) + 
   scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "", y = "Abondance en plantes") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Periode, y = S_Poll, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) + 
   scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Période", y = "Richesse spécifique en pollinisateurs") +
    theme(legend.position="none") +
  
Inv %>% ggplot(aes (x = Periode, y = Ab_Poll, color = Gestion_moment_5)) +
  geom_boxplot(alpha = 0.70) + 
   scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(#title = "Abondance de pollinisateurs en fonction des types de gestion", 
    x = "Période", y = "Abondance en pollinisateurs", color= "Type de gestion") + 
    theme(legend.position="bottom")

#esquisse::esquisser()
Inv_tib <- as_tibble(Inv)
resum_invG2 <- Inv_tib %>% 
  group_by(Periode, Gestion_2) %>% 
  summarize(mean = mean(S_Plant),
            var=var(S_Plant),
            N=n()) %>% 
  mutate(CI_S=mean+qt(0.975, N-1)*sqrt(var/N),
         CI_I=mean-qt(0.975, N-1)*sqrt(var/N))
ggplot(resum_invG2, aes(x= Periode, y=mean, colour= Gestion_2, group = Gestion_2)) + 
  geom_errorbar(aes(ymin=CI_I, ymax=CI_S),width=.15,size = 1) +
  geom_point(size= 3) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  geom_line(size = 1)+
 labs(x = "Période", 
       y = "Richesse spécifique en plantes", 
       color = "Type de gestion") 

resum_inv_SPl <- Inv_tib %>% 
  group_by(Periode, Gestion_moment_5) %>% 
  summarize(mean = mean(S_Plant),
            var=var(S_Plant),
            N=n()) %>% 
  mutate(CI_S=mean+qt(0.975, N-1)*sqrt(var/N),
         CI_I=mean-qt(0.975, N-1)*sqrt(var/N))
resum_inv_APl <- Inv_tib %>% 
  group_by(Periode, Gestion_moment_5) %>% 
  summarize(mean = mean(Ab_Plant),
            var=var(Ab_Plant),
            N=n()) %>% 
  mutate(CI_S=mean+qt(0.975, N-1)*sqrt(var/N),
         CI_I=mean-qt(0.975, N-1)*sqrt(var/N))
resum_inv_SPo <- Inv_tib %>% 
  group_by(Periode, Gestion_moment_5) %>% 
  summarize(mean = mean(S_Poll),
            var=var(S_Poll),
            N=n()) %>% 
  mutate(CI_S=mean+qt(0.975, N-1)*sqrt(var/N),
         CI_I=mean-qt(0.975, N-1)*sqrt(var/N))
resum_inv_APo <- Inv_tib %>% 
  group_by(Periode, Gestion_moment_5) %>% 
  summarize(mean = mean(Ab_Poll),
            var=var(Ab_Poll),
            N=n()) %>% 
  mutate(CI_S=mean+qt(0.975, N-1)*sqrt(var/N),
         CI_I=mean-qt(0.975, N-1)*sqrt(var/N))

ggplot(resum_inv_SPl, aes(x= Periode, y=mean, colour= Gestion_moment_5, group = Gestion_moment_5)) +
  geom_errorbar(aes(ymin=CI_I, ymax=CI_S),width=.15,size = 1) +
  geom_point(size= 3) +
          scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  geom_line(size = 1)+
  labs(x = "Période", 
       y = "Richesse spécifique en plantes", 
       color = "Type de gestion") +
  theme(legend.position="none") +
ggplot(resum_inv_APl, aes(x= Periode, y=mean, colour= Gestion_moment_5, group = Gestion_moment_5)) +
  geom_errorbar(aes(ymin=CI_I, ymax=CI_S),width=.15,size = 1) +
  geom_point(size= 3) +
          scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  geom_line(size = 1)+
  labs(x = "Période", 
       y = "Abondance en plantes", 
       color = "Type de gestion") +
  theme(legend.position="none") +
ggplot(resum_inv_SPo, aes(x= Periode, y=mean, colour= Gestion_moment_5, group = Gestion_moment_5)) + 
  geom_errorbar(aes(ymin=CI_I, ymax=CI_S),width=.15,size = 1) +
  geom_point(size= 3) +
          scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  geom_line(size = 1)+
  labs(x = "Période", 
       y = "Richesse spécifique en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="none") +
ggplot(resum_inv_APo, aes(x= Periode, y=mean, colour= Gestion_moment_5, group = Gestion_moment_5)) + 
  geom_errorbar(aes(ymin=CI_I, ymax=CI_S),width=.15,size = 1) +
  geom_point(size= 3) +
          scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  geom_line(size = 1)+
  labs(x = "Période", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

2.1.1.1.3 Influences multi-factorielles
2.1.1.1.3.1 Météo
ggplot(Inv) +
  aes(x = Meteo, y = Ab_Poll, color = Meteo) +
  geom_boxplot(alpha = 0.70) +
            scale_color_manual(values = c("Nuageux" = "#7f908c",
                                  "Alternances" = "#79ccbd",
                                  "Soleil" = "#fbcb09")) +
   labs(x = "Météo", 
       y = "Abondance en pollinisateurs") +
  theme(legend.position="none")

ggplot(Inv) +
  aes(x = Gestion_moment_5, y = Ab_Poll, color = Meteo) +
  geom_boxplot(alpha = 0.70) +
              scale_color_manual(values = c("Nuageux" = "#7f908c",
                                  "Alternances" = "#79ccbd",
                                  "Soleil" = "#fbcb09")) +
   labs(x = "Type de gestion", 
       y = "Abondance en pollinisateurs", 
       color = "Météo") +
  theme(legend.position="bottom")

2.1.1.1.3.2 Températures
ggplot(Inv) +
  aes(x = Temperature, y = Ab_Poll) +
  geom_point()+ 
  geom_smooth(se = T) + 
  labs(x = "Température", 
       y = "Abondance en pollinisateurs")

ggplot(Inv) +
  aes(x = Temperature, y = Ab_Poll, color = Gestion_moment_5) +
  geom_point()+ 
            scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  geom_smooth(se = F) + 
  labs(x = "Température", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

2.1.1.1.3.3 Activité anthropiques
ggplot(Inv) +
  aes(x = Activite, y = S_Poll) +
  geom_boxplot(alpha = 0.70)+
  labs(x = "Activité", 
       y = "Richesse spécifique en pollinisateurs") +
ggplot(Inv) +
  aes(x = Activite, y = Ab_Poll) +
  geom_boxplot(alpha = 0.70)+
  labs(x = "Activité", 
       y = "Abondance en pollinisateurs")

ggplot(Inv) +
  aes(x = Activite, y = Ab_Poll, color = Gestion_moment_5) +
  geom_boxplot(alpha = 0.70)+ 
              scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(x = "Activité", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

ggplot(Inv) +
  aes(x = Activite, y = S_Plant) +
  geom_boxplot(alpha = 0.70)+
  labs(x = "Activité", 
       y = "Richesse spécifique en plantes")+
ggplot(Inv) +
  aes(x = Activite, y = Ab_Plant) +
  geom_boxplot(alpha = 0.70)+
  labs(x = "Activité", 
       y = "Abondance en plantes")

ggplot(Inv) +
  aes(x = Activite, y = Ab_Plant, color = Gestion_moment_5) +
  geom_boxplot(alpha = 0.70)+
                scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(x = "Activité", 
       y = "Abondance en plantes", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

2.1.1.1.3.4 Heure de la journée
ggplot(Inv) +
  aes(x = Heure_debut, y = Ab_Poll, color = Gestion_2) +
  geom_point() + 
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  scale_x_discrete(breaks = c("09:00:00","10:00:00","11:00:00","12:00:00","13:00:00","14:00:00","15:00:00","16:00:00", "17:00:00"),
                   #limits = c("09:00:00","10:00:00","11:00:00","12:00:00","13:00:00","14:00:00","15:00:00","16:00:00", "17:00:00")
                   ) +
  labs(x = "Heure", 
       y = "Abondance en pollinisateurs") 

ggplot(Inv) +
  aes(x = Heure_debut, y = Ab_Poll, color = Gestion_moment_5) +
  geom_point()+ 
        scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
    scale_x_discrete(breaks = c("09:00:00","10:00:00","11:00:00","12:00:00","13:00:00","14:00:00","15:00:00","16:00:00", "17:00:00"),
                   #limits = c("09:00:00","10:00:00","11:00:00","12:00:00","13:00:00","14:00:00","15:00:00","16:00:00", "17:00:00")
                   ) +
  labs(x = "Heure", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

ggplot(Inv) +
  aes(x = Heure_fin, y = Ab_Poll, color = Gestion_2) +
  geom_point() + 
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  scale_x_discrete(breaks = c("09:30:00","10:30:00","11:30:00","12:30:00","13:30:00","14:30:00","15:35:00","16:30:00", "17:30:00"),
                   #limits = c("09:30:00","10:30:00","11:30:00","12:30:00","13:30:00","14:30:00","15:30:00","16:30:00", "17:30:00")
                   ) +
  labs(x = "Heure", 
       y = "Abondance en pollinisateurs") 

ggplot(Inv) +
  aes(x = Heure_fin, y = Ab_Poll, color = Gestion_moment_5) +
  geom_point()+ 
        scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
    scale_x_discrete(breaks = c("09:30:00","10:30:00","11:30:00","12:30:00","13:30:00","14:30:00","15:35:00","16:30:00", "17:30:00"),
                   #limits = c("09:30:00","10:30:00","11:30:00","12:30:00","13:30:00","14:30:00","15:30:00","16:30:00", "17:30:00")
                   ) +
  labs(x = "Heure", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom")

2.1.1.2 Statistiques par groupes

Création d’une fonction pour faire des statistiques descriptives par groupe

statdesbygroup=function(Y, X)
  {# 1. On récupére les niveaux de X et le nombre de niveaux
  X=as.factor(X)
  levX=levels(X)
  nlevX=length(levX)
# 2. On crée la matrice pour les statistique descriptive
  mesStats<-matrix(nrow=nlevX,ncol=7)
  colnames(mesStats)=c("N","Moyenne","Médiane","Ecart-type","Variance","Min","Max" )
  rownames(mesStats)=levels(Inv$Gestion_moment_5)
# 3. On calcule les statistiques descriptives
  for(i in 1:nlevX)
  {indlevel=which(X==levX[i])
    mesStats[i,]<-c(length(Y[indlevel]),mean(Y[indlevel]),median(Y[indlevel]),sd(Y[indlevel]),var(Y[indlevel]),min(Y[indlevel]), max(Y[indlevel]))}
# 4. On retourne les résultats 
  return(mesStats)
}


SBG_SPlant <- statdesbygroup(
  Y = c(Inv$S_Plant), 
  X = c(Inv$Gestion_moment_5))
SBG_SPlant %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Richesse spécifique de la flore") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>% kable_styling() %>%
Richesse spécifique de la flore
N Moyenne Médiane Ecart-type Variance Min Max
Graminées 11 2.090909 2 2.0225996 4.0909091 0 5
Fleuri 30 5.566667 5 2.9674479 8.8057471 1 13
Semé 18 9.055556 9 3.6213781 13.1143791 3 17
Tonte récente 26 1.923077 2 0.9347974 0.8738462 0 4
Tonte tardive 49 3.510204 3 1.3711681 1.8801020 1 6
# save_kable(file = "Output/Tableau/table_SPlant.pdf")

SBG_AbPlant  <- statdesbygroup(
  Y = c(Inv$Ab_Plant), 
  X = c(Inv$Gestion_moment_5))
SBG_AbPlant %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Abondance de la flore") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>%  kable_styling() %>%
Abondance de la flore
N Moyenne Médiane Ecart-type Variance Min Max
Graminées 11 18.72727 15.0 18.81006 353.8182 0 52
Fleuri 30 101.96667 49.0 114.09297 13017.2057 3 439
Semé 18 131.11111 110.5 81.39438 6625.0458 11 299
Tonte récente 26 30.34615 21.5 27.44586 753.2754 0 90
Tonte tardive 49 90.63265 65.0 84.70874 7175.5706 7 451
   # save_kable(file = "Output/Tableau/table_AbPlant.pdf")


SBG_SPoll <- statdesbygroup(
  Y = c(Inv$S_Poll), 
  X = c(Inv$Gestion_moment_5)) 
SBG_SPoll %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Richesse spécifique de la faune pollinisatrice") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>% kable_styling() %>%
Richesse spécifique de la faune pollinisatrice
N Moyenne Médiane Ecart-type Variance Min Max
Graminées 11 4.545454 4.0 4.321195 18.672727 0 12
Fleuri 30 8.733333 8.5 4.143406 17.167816 0 17
Semé 18 12.055556 12.0 4.165294 17.349673 4 21
Tonte récente 26 1.538461 1.0 1.272188 1.618461 0 5
Tonte tardive 49 3.244898 3.0 1.984635 3.938776 0 9
#    save_kable(file = "Output/Tableau/table_SPoll.pdf")

SBG_AbPoll <- statdesbygroup(
  Y = c(Inv$Ab_Poll), 
  X = c(Inv$Gestion_moment_5))
SBG_AbPoll %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Abondance de la faune pollinisatrice") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>% kable_styling() %>%
Abondance de la faune pollinisatrice
N Moyenne Médiane Ecart-type Variance Min Max
Graminées 11 9.454546 4.0 12.532866 157.072727 0 42
Fleuri 30 18.400000 17.5 11.278909 127.213793 0 43
Semé 18 34.333333 35.0 14.868047 221.058823 4 61
Tonte récente 26 2.038461 1.0 1.865063 3.478461 0 7
Tonte tardive 49 7.469388 6.0 6.010761 36.129252 0 26
#    save_kable(file = "Output/Tableau/table_AbPoll.pdf")
statdesbygroup=function(Y, X)
  {# 1. On récupére les niveaux de X et le nombre de niveaux
  X=as.factor(X)
  levX=levels(X)
  nlevX=length(levX)
# 2. On crée la matrice pour les statistique descriptive
  mesStats<-matrix(nrow=nlevX,ncol=7)
  colnames(mesStats)=c("N","Moyenne","Médiane","Ecart-type","Variance","Min","Max" )
  rownames(mesStats)=levels(Inv$Periode)
# 3. On calcule les statistiques descriptives
  for(i in 1:nlevX)
  {indlevel=which(X==levX[i])
    mesStats[i,]<-c(length(Y[indlevel]),mean(Y[indlevel]),median(Y[indlevel]),sd(Y[indlevel]),var(Y[indlevel]),min(Y[indlevel]), max(Y[indlevel]))}
# 4. On retourne les résultats 
  return(mesStats)
}


SBG_SPlant_Periode <- statdesbygroup(
  Y = c(Inv$S_Plant), 
  X = c(Inv$Periode))
SBG_SPlant_Periode %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Richesse spécifique de la flore") %>%
  kable_classic(full_width = F, html_font = "Cambria")  #  %>% kable_styling() %>%
Richesse spécifique de la flore
N Moyenne Médiane Ecart-type Variance Min Max
Juin 46 4.782609 4 3.450688 11.907246 0 17
Mi-juillet 46 4.108696 3 3.121455 9.743478 0 13
Fin juillet 42 3.952381 3 2.819379 7.948897 0 13
# save_kable(file = "Output/Tableau/table_SPlant_Periode.pdf")

SBG_AbPlant_Periode  <- statdesbygroup(
  Y = c(Inv$Ab_Plant), 
  X = c(Inv$Periode))
SBG_AbPlant_Periode %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Abondance de la flore") %>%
  kable_classic(full_width = F, html_font = "Cambria") #  %>%  kable_styling() %>%
Abondance de la flore
N Moyenne Médiane Ecart-type Variance Min Max
Juin 46 119.36957 86.0 111.70484 12477.971 0 451
Mi-juillet 46 67.41304 37.5 73.57553 5413.359 0 299
Fin juillet 42 53.88095 37.0 53.42248 2853.961 0 239
 #    save_kable(file = "Output/Tableau/table_AbPlant_Periode.pdf")


SBG_SPoll_Periode <- statdesbygroup(
  Y = c(Inv$S_Poll), 
  X = c(Inv$Periode)) 
SBG_SPoll_Periode %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Richesse spécifique de la faune pollinisatrice") %>%
  kable_classic(full_width = F, html_font = "Cambria")#  %>% kable_styling() %>%
Richesse spécifique de la faune pollinisatrice
N Moyenne Médiane Ecart-type Variance Min Max
Juin 46 6.217391 5.5 4.939244 24.39614 0 21
Mi-juillet 46 5.413043 4.0 5.144905 26.47005 0 19
Fin juillet 42 4.595238 3.0 3.876505 15.02729 0 16
 #   save_kable(file = "Output/Tableau/table_SPoll_Periode.pdf")

SBG_AbPoll_Periode <- statdesbygroup(
  Y = c(Inv$Ab_Poll), 
  X = c(Inv$Periode))
SBG_AbPoll_Periode %>% 
  kbl(row.names=Inv$Gestion_moment_5, caption = "Abondance de la faune pollinisatrice") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>% kable_styling() %>%
Abondance de la faune pollinisatrice
N Moyenne Médiane Ecart-type Variance Min Max
Juin 46 13.391304 10.5 11.73689 137.7546 0 41
Mi-juillet 46 14.478261 8.0 17.16552 294.6551 0 61
Fin juillet 42 9.785714 4.0 10.66916 113.8310 0 42
 # save_kable(file = "Output/Tableau/table_AbPoll_Periode.pdf")

2.1.2 Courbes d’accumulation

#Préparations données
especes <- Inventaire[,c(19:166)]

Esp_Plant <- Inventaire[,c(19:67)]
Esp_Poll <- Inventaire[,c(68:166)]

Inv_red <- Inventaire[,c(3:5,10:11,14,167:170)]

2.1.2.1 Plantes

# accumcomp(Esp_Plant, y = Inv_red, factor = "Gestion_2", col = c("#aa1e0f","#12661f"), rainbow = F, xlim = c(1,90), plotit = TRUE, labelit = F, legend = F, xlab = "Nombre d'échantillonnages", ylab = "Nombre d'espèces")[1]
# legend("topright", legend = c("Fauche", "Tonte"), col = c("#aa1e0f", "#12661f"), lty = 1, xpd = TRUE, inset = c(0,-0.2), horiz = F)

Accum.Plantes_G2 <- accumcomp(Esp_Plant, y=Inv_red, factor='Gestion_2',
    method='exact', conditioned=FALSE, plotit=FALSE)
accum.long_Plantes_G2 <- accumcomp.long(Accum.Plantes_G2, ci=NA, label.freq=1)
plG2 <- ggplot(data=accum.long_Plantes_G2, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
    scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1.5) +
    geom_point(data=subset(accum.long_Plantes_G2, labelit==TRUE), 
               aes(colour=Grouping, shape=Grouping), size=2.5) +
      scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
                show.legend=FALSE) + 
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
    labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de plantes", colour = "Type de gestion", shape = "Type de gestion") + 
  theme(legend.position = "bottom")

# accumcomp(Esp_Plant, y = Inv_red, factor = "Gestion_moment_5", col = c("darkgreen", "green"), xlim = c(1,70), plotit = T, labelit = F, rainbow = F, legend = F, xlab = "Nombre d'échantillonages", ylab = "Nombre d'espèces")[1] 
# legend("topright", legend = c("Fleuri", "Graminées", "Semé","Tonte récente", "Tonte tardive"), col = rainbow(5), lty = 1, xpd = TRUE, inset = c(0,-0.2), horiz = F)

Accum.Plantes_G5 <- accumcomp(Esp_Plant, y=Inv_red, factor='Gestion_moment_5',
    method='exact', conditioned=FALSE, plotit=FALSE)
accum.long_Plantes_G5 <- accumcomp.long(Accum.Plantes_G5, ci=NA, label.freq=1)
accum.long_Plantes_G5$Grouping <-  fct_relevel(accum.long_Plantes_G5$Grouping,c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"))
plG5 <- ggplot(data=accum.long_Plantes_G5, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
    scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1.5) +
    geom_point(data=subset(accum.long_Plantes_G5, labelit==T), 
               aes(colour=Grouping, shape=Grouping), size=2.5) +
    scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) +
    geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
                show.legend=FALSE) + 
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
    labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de plantes", colour = "Type de gestion", shape = "Type de gestion") +
  theme(legend.position = "bottom")

plG2 + plG5

2.1.2.2 Pollinisateurs

# accumcomp(Esp_Poll, y = Inv_red, factor = "Gestion_2", xlim = c(1,90), plotit = T, labelit = F, rainbow = T, legend = F, xlab = "Nombre d'échantillonages", ylab = "Nombre d'espèces")[1] 
# legend("topright", legend = c("Fauche", "Tonte"), col = rainbow(2), lty = 1, xpd = TRUE, inset = c(0,-0.2), horiz = F)

Accum.Poll_G2 <- accumcomp(Esp_Poll, y=Inv_red, factor='Gestion_2',
    method='exact', conditioned=FALSE, plotit=FALSE)
accum.long_Poll_G2 <- accumcomp.long(Accum.Poll_G2, ci=NA, label.freq=1)
pollG2 <- ggplot(data=accum.long_Poll_G2, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
    scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1.5) +
    geom_point(data=subset(accum.long_Poll_G2, labelit==TRUE), 
               aes(colour=Grouping, shape=Grouping), size=2.5) +
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 4)) +
    geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
                show.legend=FALSE) + 
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
    labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de pollinisateurs", colour = "Type de gestion", shape = "Type de gestion") + 
  theme(legend.position = "bottom")

# accumcomp(Esp_Poll, y = Inv_red, factor = "Gestion_moment_5", col = c("blue"), xlim = c(1,70), xlab = "Nombre d'échantillonages", ylab = "Nombre d'espèces", plotit = T, labelit = F, legend = F, rainbow = F)[1] 
# legend("topright", legend = c("Fleuri", "Graminées", "Semé","Tonte récente", "Tonte tardive"), col = rainbow(5), lty = 1, xpd = TRUE, inset = c(0,-0.2), horiz = F)

Accum.Poll_G5 <- accumcomp(Esp_Poll, y=Inv_red, factor='Gestion_moment_5',
    method='exact', conditioned=FALSE, plotit=FALSE)
accum.long_Poll_G5 <- accumcomp.long(Accum.Poll_G5, ci=NA, label.freq=1)
accum.long_Poll_G5$Grouping <-  fct_relevel(accum.long_Poll_G5$Grouping,c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"))

pollG5 <- ggplot(data=accum.long_Poll_G5, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
    scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1.5) +
    geom_point(data=subset(accum.long_Poll_G5, labelit==T), 
               aes(colour=Grouping, shape=Grouping), size=2.5) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 15,
                                  "Semé" = 17,
                                  "Tonte récente" = 4,
                                  "Tonte tardive" = 8)) + 
    geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
                show.legend=FALSE) + 
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
    labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de pollinisateurs", colour = "Type de gestion", shape = "Type de gestion") +
  theme(legend.position = "bottom")

pollG2 + pollG5

2.1.3 Classes de pollinisateurs

Inv_Classes_pl <- Inventaire %>% 
  rownames_to_column(var="Site_gestion_date") %>% 
  select(Site_gestion_date, Aglais_io:Volucella_zonaria) %>% 
  pivot_longer(cols = Aglais_io:Volucella_zonaria,
               names_to = "Sp_poll",
               values_to = "Donnees") %>% 
  mutate(Sp_poll = as.factor(Sp_poll))

Inv_Classes_join_pw <- left_join(Inv_Classes_pl, Classes, by = c("Sp_poll" = "Sp_Pollinisateurs")) %>% 
   pivot_wider(names_from = Classe_Poll,
              values_from = Donnees,
              values_fill = 0) %>% 
  select(-Sp_poll)
Inv_Classes <- aggregate(.~ Site_gestion_date, data=Inv_Classes_join_pw, FUN=sum)

Inv_noms <- Inventaire %>% 
  rownames_to_column(var="Site_gestion_date") %>% 
  select(Site, Site_gestion_date,
         Nombre_quadrats,
         Gestion_2, Parcelle, Gestion_4,
         Mixte_isole,
         Gestion_moment_4, Gestion_moment_5,
         Periode,
         Area_gis_m_sq,
         Quartier,
         Activite,
         Temperature, Meteo)

Inv_fulljoin <- full_join(Site_gestion, Inv_noms, by = "Site_gestion_date") %>% 
  select(-Site_gestion_date_Quadrat) %>% 
  distinct(Site_gestion_date, .keep_all = TRUE)

Inventaire_Classes <- full_join(Inv_fulljoin, Inv_Classes, by = "Site_gestion_date")

Inventaire_Classes$S_class <- specnumber(Inventaire_Classes[16:21])
Inventaire_Classes$Ab_class <- rowSums(Inventaire_Classes[16:21])

2.2 Réseaux d’interactions

2.2.1 Préparation des données et tests

Inv_noms <- Inventaire %>% 
  rownames_to_column(var="Site_gestion_date") %>% 
  select(Site, Site_gestion_date,
         Nombre_quadrats,
         Gestion_2, Parcelle, Gestion_4,
         Mixte_isole,
         Gestion_moment_4, Gestion_moment_5,
         Periode,
         Area_gis_m_sq,
         Quartier,
         Activite,
         Temperature, Meteo)

Inv_fulljoin <- full_join(Site_gestion, Inv_noms, by = "Site_gestion_date")

Interactions_Gestion <- full_join(Inv_fulljoin, Interactions, by = "Site_gestion_date_Quadrat")

Interactions_Classes <- left_join(Interactions_Gestion, Classes, by = "Sp_Pollinisateurs")

Interactions_Gestion$Sp_Plantes <- sub("_", " ", Interactions_Gestion$Sp_Plantes)
Interactions_Gestion$Sp_Pollinisateurs <- sub("_", " ", Interactions_Gestion$Sp_Pollinisateurs)
Interactions_Gestion$Sp_Plantes <- sub("_", " - ", Interactions_Gestion$Sp_Plantes)
Interactions_Gestion$Sp_Pollinisateurs <- sub("_", " - ", Interactions_Gestion$Sp_Pollinisateurs)
Interactions_Gestion$Sp_Plantes <- sub("._", " ", Interactions_Gestion$Sp_Plantes)
Interactions_Gestion$Sp_Plantes <- sub("_", " ", Interactions_Gestion$Sp_Plantes)
Interactions_Gestion$Sp_Pollinisateurs <- sub("_", " ", Interactions_Gestion$Sp_Pollinisateurs)

Interactions_Classes$Sp_Plantes <- sub("_", " ", Interactions_Classes$Sp_Plantes)
Interactions_Classes$Sp_Pollinisateurs <- sub("_", " ", Interactions_Classes$Sp_Pollinisateurs)
Interactions_Classes$Sp_Plantes <- sub("_", " - ", Interactions_Classes$Sp_Plantes)
Interactions_Classes$Sp_Pollinisateurs <- sub("_", " - ", Interactions_Classes$Sp_Pollinisateurs)
Interactions_Classes$Sp_Plantes <- sub("._", " ", Interactions_Classes$Sp_Plantes)
Interactions_Classes$Sp_Plantes <- sub("_", " ", Interactions_Classes$Sp_Plantes)
Interactions_Classes$Sp_Pollinisateurs <- sub("_", " ", Interactions_Classes$Sp_Pollinisateurs)

2.2.1.1 Tests

Interact_esp <- Interactions_Gestion %>% 
  group_by(Site_gestion_date, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions)) %>% 
  mutate(sum = as.numeric(sum))

network_Hemptinne_Tonte_Juin <- Interact_esp %>% 
  filter(Site_gestion_date == "Hemptinne_Tonte_Juin") %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  arrange(Sp_Plantes)

network_Mendel_Tonte_finJuillet <- Interact_esp %>% 
  filter(Site_gestion_date == "Mendel_Tonte_finJuilllet") %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  column_to_rownames(var="Sp_Plantes")


network_Reaumur_Fauche_miJuillet <- Interact_esp %>% 
  filter(Site_gestion_date == "Reaumur_Fauche_miJuillet") %>% 
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Site_gestion_date) %>% 
  column_to_rownames(var="Sp_Plantes")

network_Reaumur_Fauche_miJuillet %>% 
  kbl() %>% 
  kable_classic(full_width = F, html_font = "Cambria")
plotweb(network_Reaumur_Fauche_miJuillet, text.rot = 90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-0.5,2))

#plotweb(network_Reaumur_Fauche_miJuillet,col.high=rainbow(15),col.low=rainbow(8),text.rot=0,y.width.high=0.07, y.width.low=0.07,y.lim=c(-0.5,2))

networklevel(network_Reaumur_Fauche_miJuillet)%>% 
  kbl() %>% 
  kable_classic(full_width = F, html_font = "Cambria") 

2.2.2 Interactions

Interactions_Gestion %>% ggplot(aes (x = Gestion_2, y = N_Interactions, color = Gestion_2)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Periode) + 
   scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  theme(legend.position = "none") +
  
Interactions_Gestion %>% ggplot(aes (x = Periode, y = N_Interactions, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Gestion_2) + 
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  theme(legend.position = "none") 

Interactions_Gestion %>% ggplot(aes (x = Gestion_moment_5, y = N_Interactions, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Periode) + 
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "none") +
  
Interactions_Gestion %>% ggplot(aes (x = Periode, y = N_Interactions, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Gestion_moment_5) + 
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  theme(legend.position = "none") 

Interactions_Gestion %>% ggplot(aes (x = Gestion_moment_5, y = N_Interactions, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
   scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) + 
  theme(legend.position = "none") +

Interactions_Gestion %>% ggplot(aes (x = Periode, y = N_Interactions, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "none") 

Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Gestion_moment_5, y = n, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Periode) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "none") +
  
Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Periode, y = n, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
  facet_wrap(~ Gestion_moment_5) +
   scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) + 
  theme(legend.position = "none") 

Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Periode, y = n, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "none") +
  
Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Gestion_moment_5, y = n, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
   scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) + 
  theme(legend.position = "none") 

Interactions_Gestion %>% ggplot(aes (x = Qtte_Plantes, y = N_Interactions, color = Gestion_moment_5)) + 
  geom_point() + 
  geom_smooth(method = "glm", se = T) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "none") 

2.2.3 For-Loop réseaux

Loop <-  Interactions_Gestion %>% 
  filter(Site_gestion_date != "Aula_Tonte_miJuillet",
         Site_gestion_date != "AvGL_Tonte_finJuillet",
         Site_gestion_date != "AvGL_Tonte_miJuillet",
         Site_gestion_date != "BlancsChevaux_Tonte_finJuillet",
         Site_gestion_date != "BlancsChevaux_Tonte_miJuillet",
         Site_gestion_date != "Curie_Tonte_miJuillet",
         Site_gestion_date != "Gare_bus_Tonte_finJuillet",
         Site_gestion_date != "Lauzelle_Fauche_Juin",
         Site_gestion_date != "Lauzelle_Fauche_miJuillet",
         Site_gestion_date != "Lavoisier_Fauche_miJuillet",
         Site_gestion_date != "Mendel_Tonte_finJuillet",
         Site_gestion_date != "Mendel_Tonte_miJuillet",
         Site_gestion_date != "Parc_Lapins_Fauche_Juin",
         Site_gestion_date != "Parc_Lapins_Fauche_miJuillet") %>% 
  mutate(Site_gestion_date = as.factor(Site_gestion_date),
         Sp_Plantes = as.factor(Sp_Plantes),
         Sp_Pollinisateurs = as.factor(Sp_Pollinisateurs))

loop_sum <- Loop %>% 
  group_by(Site_gestion_date, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions))

col_names <- colnames(loop_sum)
col_names <- col_names[-1]

par(font = 3)


for (x in levels(loop_sum$Site_gestion_date)) {
  pdf(paste0("Output/Reseaux_loop/Plots/", x, ".pdf")) # ouvrir le fichier
  plot_nw <- plotweb(loop_sum %>% 
                       filter(Site_gestion_date == x) %>% 
                       filter(sum!=0) %>% 
                       select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
                       arrange(Sp_Pollinisateurs) %>% 
                       pivot_wider(names_from = Sp_Pollinisateurs,
                                   values_from = sum,
                                   values_fill = 0) %>% 
                       select(where(~ any(. != 0))) %>% 
                       arrange(Sp_Plantes) %>% 
                       as.data.frame() %>%
                       select(-Site_gestion_date) %>% 
                       column_to_rownames(var="Sp_Plantes"), 
                     high.lablength= 35, low.lablength=27,
                     text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))
  plot_nw # imprimer le graphique
  dev.off() # fermer le fichier 
}

for (x in levels(loop_sum$Site_gestion_date)) {
  nw_lv <- networklevel(loop_sum %>% 
                       filter(Site_gestion_date == x) %>% 
                       filter(sum!=0) %>% 
                       select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
                       arrange(Sp_Pollinisateurs) %>% 
                       pivot_wider(names_from = Sp_Pollinisateurs,
                                   values_from = sum,
                                   values_fill = 0) %>% 
                       select(where(~ any(. != 0))) %>% 
                       arrange(Sp_Plantes) %>% 
                       as.data.frame() %>%
                       select(-Site_gestion_date) %>% 
                       column_to_rownames(var="Sp_Plantes"))
  write.csv2(nw_lv, file = paste0("Output/Reseaux_loop/Nw_Levels/", x, ".csv"))
}

2.2.4 Fauche vs Tonte G2

Interact_G2 <- Interactions_Gestion %>% 
  group_by(Gestion_2, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions)) %>% 
  mutate(sum = as.numeric(sum))

par(font = 3)
network_fauche <- Interact_G2 %>% 
  filter(Gestion_2 == "Fauche") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
# plotweb(network_fauche, high.lablength= 35, low.lablength=27, low.y = -0.1, high.y = 0.7, col.high=c("#aa1e0f"),col.low=c("#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,2.5))
plotweb(network_fauche, col.high=c("#aa1e0f"),col.low=c("#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

#plotweb(network_fauche,col.high=c("black","yellow"),col.low=c("light green", "dark green"),text.rot=90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-1,3))
#plotweb(network_fauche,col.high=rainbow(47),col.low=rainbow(47),text.rot=90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-1,3))

# #plotweb(network_fauche, method = "normal",
#         col.interaction=c(rep("grey50", 2),"darkgoldenrod1",rep("grey50", 2), "white"), 
#         col.high = c(rep("grey50", 2),"darkgoldenrod1",rep("grey50", 2), "white"),  
#         bor.col.high = c(rep("grey50", 2),"darkgoldenrod1",rep("grey50", 2) ,"white"),
#         bor.col.interaction = c(rep("grey50", 2),"darkgoldenrod1",rep("grey50", 2), "white"), 
#         col.low=c("black", "white"), bor.col.low = c("black", "white"), 
#         text.high.col = c(rep("black", 5), "white"), text.low.col =  c("black", "white"),
#         text.rot = 90)
#title(main = "Réseau d'interaction fauche")

# networklevel(network_fauche, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) %>% 
#   kbl() %>%
#   kable_classic(full_width = F, html_font = "Cambria")

network_tonte <- Interact_G2 %>% 
  filter(Gestion_2 == "Tonte") %>% 
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_tonte,col.high=c("#12661f"),col.low=c("#12661f"),text.rot=90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-1,3.5))

#plotweb(network_tonte,col.high=rainbow(40),col.low=rainbow(10),text.rot=90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-1,3))

# networklevel(network_tonte, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) %>% 
#   kbl() %>%
#   kable_classic(full_width = F, html_font = "Cambria")


Fauche <- networklevel(network_fauche, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) 
Tonte <- networklevel(network_tonte, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
Table_NW_G2 <- cbind(Fauche, Tonte)
colnames(Table_NW_G2) <- c("Fauche", "Tonte")

Table_NW_G2 %>% 
  kbl(caption = "Gestion - biclassification") %>%
  kable_classic(full_width = F, html_font = "Cambria")  #%>% kable_styling() %>%
Gestion - biclassification
Fauche Tonte
connectance 0.1181478 0.2943723
web asymmetry 0.4590164 0.6500000
nestedness 5.7725375 21.9889323
linkage density 6.9668799 2.1539505
Fisher alpha 102.2384405 13.8299152
Shannon diversity 4.5609652 1.6289212
interaction evenness 0.5711813 0.2993010
H2 0.4291655 0.6866345
robustness.HL 0.8092341 0.8664278
robustness.LL 0.6852618 0.6251094
generality.HL 5.2007056 1.3405415
vulnerability.LL 8.7330541 2.9673596
# save_kable(file = "Output/Tableau/NW_G2.pdf")

2.2.5 Gestion moment 5

Interact_Gm5 <- Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions)) %>% 
  mutate(sum = as.numeric(sum))

par(font = 3)
network_gram <- Interact_Gm5 %>% 
  filter(Gestion_moment_5 == "Graminées") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_moment_5) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_gram, col.high=c("#fbcb09"),col.low=c("#fbcb09"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_gram, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_fleuri <- Interact_Gm5 %>% 
  filter(Gestion_moment_5 == "Fleuri") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_moment_5) %>% 
  column_to_rownames(var="Sp_Plantes")
# plotweb(network_fleuri, col.high=c("#ff7207"),col.low=c("#ff7207"),high.lablength= 30, low.lablength=35, low.y = -0.25, text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))
plotweb(network_fleuri, col.high=c("#ff7207"),col.low=c("#ff7207"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_fleuri, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_seme <- Interact_Gm5 %>% 
  filter(Gestion_moment_5 == "Semé") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_moment_5) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_seme, col.high=c("#de1e21"),col.low=c("#de1e21"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_seme, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_TonRec <- Interact_Gm5 %>% 
  filter(Gestion_moment_5 == "Tonte récente") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_moment_5) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_TonRec, col.high=c("#6abe1d"), col.low=c("#6abe1d"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_TonRec, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_TonTard <- Interact_Gm5 %>% 
  filter(Gestion_moment_5 == "Tonte tardive") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Gestion_moment_5) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_TonTard, col.high=c("#2b790c"), col.low=c("#2b790c"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_TonTard, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))


Graminees <- networklevel(network_gram, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) %>% 
  as.data.frame()
Fleuri <- networklevel(network_fleuri, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))%>% 
  as.data.frame()
Seme <- networklevel(network_seme, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) %>% 
  as.data.frame()
Tonte_recente <- networklevel(network_TonRec, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))%>% 
  as.data.frame()
Tonte_tardive <- networklevel(network_TonTard, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) %>% 
  as.data.frame()

Table_NW_G5 <- cbind(Graminees, Fleuri, Seme, Tonte_recente, Tonte_tardive)
colnames(Table_NW_G5) <- c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive")
Table_NW_G5 %>%
  kbl(caption = "Gestion - pentaclassification") %>%
  kable_classic(full_width = F, html_font = "Cambria") 
Gestion - pentaclassification
Graminées Fleuri Semé Tonte récente Tonte tardive
connectance 0.2407407 0.1142534 0.1418764 0.4722222 0.2857143
web asymmetry 0.6363636 0.4468085 0.4250000 0.6000000 0.6500000
nestedness 33.9172270 8.5064130 9.7964195 20.3768787 23.5572002
linkage density 4.5620168 5.5351438 4.9818744 1.9228986 2.1270847
Fisher alpha 15.7290348 71.1656509 54.1504976 4.6059563 13.6693767
Shannon diversity 3.2435071 4.2667950 3.8437780 1.2878123 1.6404026
interaction evenness 0.6375323 0.5706099 0.5354536 0.3593709 0.3014106
H2 0.7336450 0.5114385 0.4496612 0.8104420 0.6990216
robustness.HL 0.8496203 0.7821565 0.7777715 0.7576407 0.8713629
robustness.LL 0.5716149 0.6460864 0.6755235 0.5618750 0.6209114
generality.HL 1.4745200 3.8796971 3.3251203 1.1012144 1.3308515
vulnerability.LL 7.6495137 7.1905906 6.6386285 2.7445828 2.9233178

2.2.6 Période

Interact_Periode <- Interactions_Gestion %>% 
  group_by(Periode, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions)) %>% 
  mutate(sum = as.numeric(sum))

par(font = 3)

network_Juin <- Interact_Periode %>% 
  filter(Periode == "Juin") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_Juin, col.high=c("#74a9cf"), col.low=c("#74a9cf"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_Juin, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_miJuil <- Interact_Periode %>% 
  filter(Periode == "Mi-juillet") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_miJuil, col.high=c("#2b8cbe"), col.low=c("#2b8cbe"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_miJuil, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))

network_finJuil <- Interact_Periode %>% 
  filter(Periode == "Fin juillet") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_finJuil, col.high=c("#045a8d"), col.low=c("#045a8d"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

# networklevel(network_finJuil, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))



juin <- networklevel(network_Juin, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) 
mijuillet <- networklevel(network_miJuil, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
finjuillet <- networklevel(network_finJuil, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality")) 

Table_NW_P <- cbind(juin, mijuillet, finjuillet)
colnames(Table_NW_P) <- c("Juin", "Mi-juillet", "Fin juillet")
Table_NW_P %>%
  kbl(caption = "Période") %>%
  kable_classic(full_width = F, html_font = "Cambria") 
Période
Juin Mi-juillet Fin juillet
connectance 0.1352785 0.1303030 0.1064815
web asymmetry 0.3809524 0.3924051 0.3846154
nestedness 10.1552918 8.9542173 8.7682013
linkage density 4.7004410 4.2604178 3.6859752
Fisher alpha 61.9488092 44.7255342 40.7488484
Shannon diversity 3.7803507 3.5367435 3.1842605
interaction evenness 0.5165444 0.4922134 0.4442924
H2 0.5157954 0.4666628 0.5867317
robustness.HL 0.8013260 0.7962863 0.7359547
robustness.LL 0.6907693 0.6650232 0.6303276
generality.HL 3.9909709 3.5874657 3.0955332
vulnerability.LL 5.4099111 4.9333699 4.2764171

2.2.7 Période x Gestion moment 2

Interact_Periode_g2 <- Interactions_Gestion %>% 
  group_by(Periode, Gestion_2, Sp_Plantes, Sp_Pollinisateurs) %>% 
  summarise(sum = sum(N_Interactions)) %>% 
  mutate(sum = as.numeric(sum))

par(font = 3)

network_Juin_fauche <- Interact_Periode_g2 %>% 
  filter(Periode == "Juin" &
         Gestion_2 == "Fauche") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_Juin_fauche, col.high=c("#74a9cf","#aa1e0f"), col.low=c("#74a9cf","#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_Juin_fauche, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.1272727            0.3580247            9.2741647 
##      linkage density         Fisher alpha    Shannon diversity 
##            5.9304150           68.8198247            4.4090059 
## interaction evenness                   H2        robustness.HL 
##            0.6068472            0.4613997            0.7928581 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.6647254            4.6183087            7.2425214
network_Juin_tonte <- Interact_Periode_g2 %>% 
  filter(Periode == "Juin" &
         Gestion_2 == "Tonte") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_Juin_tonte, col.high=c("#74a9cf","#12661f"), col.low=c("#74a9cf","#12661f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_Juin_tonte, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.3115942            0.5862069           22.6470969 
##      linkage density         Fisher alpha    Shannon diversity 
##            2.0355532           10.0881095            1.7214311 
## interaction evenness                   H2        robustness.HL 
##            0.3493693            0.7407693            0.8278930 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.6112332            1.3961494            2.6749570
network_miJui_fauche <- Interact_Periode_g2 %>% 
  filter(Periode == "Mi-juillet" &
         Gestion_2 == "Fauche") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_miJui_fauche, col.high=c("#2b8cbe","#aa1e0f"), col.low=c("#2b8cbe","#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_miJui_fauche, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.1279264            0.3866667            8.4918294 
##      linkage density         Fisher alpha    Shannon diversity 
##            4.5996354           43.6299928            3.7923862 
## interaction evenness                   H2        robustness.HL 
##            0.5351385            0.5190806            0.7764687 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.6631771            3.6206267            5.5786441
network_miJui_tonte <- Interact_Periode_g2 %>% 
  filter(Periode == "Mi-juillet" &
         Gestion_2 == "Tonte") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_miJui_tonte, col.high=c("#2b8cbe","#12661f"), col.low=c("#2b8cbe","#12661f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_miJui_tonte, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.3000000            0.5652174           23.8948547 
##      linkage density         Fisher alpha    Shannon diversity 
##            2.1902785            5.7177643            1.4246274 
## interaction evenness                   H2        robustness.HL 
##            0.3165973            0.7622993            0.7690623 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.5775509            1.1260362            3.2545208
network_finJui_fauche <- Interact_Periode_g2 %>% 
  filter(Periode == "Fin juillet" &
         Gestion_2 == "Fauche") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_finJui_fauche, col.high=c("#045a8d","#aa1e0f"), col.low=c("#045a8d","#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_finJui_fauche, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.1072727            0.3888889            8.7938114 
##      linkage density         Fisher alpha    Shannon diversity 
##            4.7715399           42.7795835            3.9215302 
## interaction evenness                   H2        robustness.HL 
##            0.5599734            0.5423388            0.7291346 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.6249477            3.1904334            6.3526464
network_finJui_tonte <- Interact_Periode_g2 %>% 
  filter(Periode == "Fin juillet" &
         Gestion_2 == "Tonte") %>%
  filter(sum!=0) %>% 
  select(Sp_Pollinisateurs,Sp_Plantes,sum) %>% 
  arrange(Sp_Pollinisateurs) %>% 
  pivot_wider(names_from = Sp_Pollinisateurs,
              values_from = sum,
              values_fill = 0) %>% 
  select(where(~ any(. != 0))) %>% 
  arrange(Sp_Plantes) %>% 
  as.data.frame() %>%
  select(-Periode) %>% 
  select(-Gestion_2) %>% 
  column_to_rownames(var="Sp_Plantes")
plotweb(network_finJui_tonte, col.high=c("#045a8d","#12661f"), col.low=c("#045a8d","#12661f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3))

networklevel(network_finJui_tonte, index=c("connectance", "web asymmetry", "nestedness", "linkage density", "Fisher alpha","Shannon diversity", "interaction evenness","H2","robustness", "robustness", "generality"))
##          connectance        web asymmetry           nestedness 
##            0.2368421            0.5200000           28.2961535 
##      linkage density         Fisher alpha    Shannon diversity 
##            1.4223746            6.0066788            0.9281065 
## interaction evenness                   H2        robustness.HL 
##            0.1959602            0.7960476            0.7729940 
##        robustness.LL        generality.HL     vulnerability.LL 
##            0.5851331            1.2289000            1.6158491

2.3 Expérience supplémentaire: Tonte

#Mise en facteur des variables 
expe_Tonte = expe_Tonte %>% 
  mutate(Site = as.factor(Site),
         Date = as.factor(Date))

#Tonte$Date <- dmy(Tonte$Date)
summary(expe_Tonte)

expe_Tonte$S <- specnumber(expe_Tonte[5:11])
expe_Tonte$Ab <- rowSums(expe_Tonte[5:11])


# mettre une ligne par espèce
Tonte_pl <- expe_Tonte %>%
  pivot_longer(cols = Bellis_perennis:Trifolium_repens,
               names_to = "Espèces",
               values_to = "Qtté")

Tonte_pl$Qtte_trans <- Tonte_pl$Qtté
Tonte_pl[Tonte_pl == 0] <- NA
expe_Tonte %>% ggplot(aes (x = Jour_af_tonte, y = Ab, color=Site)) + 
  geom_line() +  
  labs(title = "Abondance de fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Abondance de fleurs") +
      theme(legend.position="bottom") +
expe_Tonte %>% ggplot(aes (x = Jour_af_tonte, y = Ab, color=Site)) + 
  geom_smooth()+
  labs(title = "Abondance de fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Abondance de fleurs") +
      theme(legend.position="none")

Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = S)) + 
  facet_wrap(~Site) +
  geom_smooth(se = F)+
  labs(title = "Abondance de fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Abondance de fleurs") +
      theme(legend.position="bottom") +
Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = S, color = Site)) + 
  geom_point() +
  geom_smooth(se = F)+
  labs(title = "Richesse spécifique en fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Richesse spécifique en fleurs") +
      theme(legend.position="bottom")

Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = Qtté, color=Espèces)) + 
  geom_smooth(se=F)+
  labs(title = "Abondance de fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Abondance de fleurs") +
      theme(legend.position="bottom") 

Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = Qtté, color=Espèces)) + 
  facet_wrap(~Site) +
  geom_smooth(se = F)+
  labs(title = "Abondance de fleurs après la tonte selon les sites" , x = "Jour après la tonte", y = "Abondance de fleurs") +
      theme(legend.position="bottom")

3 Statistiques

3.1 Multivariée

3.1.1 ACP

3.1.1.1 ACP - Inv

gestion_2 <- as.factor(Inv$Gestion_2)
gestion_5 <- as.factor(Inv$Gestion_moment_5)
periode <- as.factor(Inv$Periode)
activite <- as.factor(Inv$Activite)
meteo <- as.factor(Inv$Meteo)
site <- as.factor(Inv$Site)
quartier <- as.factor(Inv$Quartier)

NumVar <- sapply(Inv, is.numeric)
df <- Inv[,NumVar] # %>% 
  # select(!c(Heure_fin,Heure_debut))
n <- nrow(df) 
p <- ncol(df)

acp <- dudi.pca(df, scannf = F, nf = p)
fviz_pca_biplot(acp)

screeplot(acp, main = "Valeurs propres")

fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_var(acp, 
             col.var = "cos2", axes = c(2,3),
             title = "Cercle de corrélation et qualité de représentation dans le plan 2-3", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_var(acp, 
             col.var = "cos2", axes = c(4,5),
             title = "Cercle de corrélation et qualité de représentation dans le plan 4-5",
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_var(acp, 
             col.var = "cos2", axes = c(5,6),
             title = "Cercle de corrélation et qualité de représentation dans le plan 5-6", 
             gradient.cols=c("red", "gold", "forestgreen"))

fviz_pca_ind(acp, repel = T,
             labelsize = 2, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 1-2")

fviz_pca_ind(acp, axes = c(2,3), 
             labelsize = 0, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 2-3")

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
      aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
  aes(color = gestion_5, shape = gestion_5, fill = gestion_5)+ 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = periode, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable periode") +
    aes(color = periode, shape = periode, fill = periode) +
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  scale_fill_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d"))

fviz_pca_ind(acp, col.var = "var",
             labelsize = 0, 
             col.ind = activite, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable activite") +
aes(color = activite, shape = activite, fill = activite) +
    scale_color_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) +
    scale_fill_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
    aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = quartier, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable quartier") 

summary(acp)
## Class: pca dudi
## Call: dudi.pca(df = df, scannf = F, nf = p)
## 
## Total inertia: 7
## 
## Eigenvalues:
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##  3.4909  1.2737  0.7696  0.6520  0.4721 
## 
## Projected inertia (%):
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##  49.871  18.195  10.995   9.314   6.745 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   49.87   68.07   79.06   88.37   95.12 
## 
## (Only 5 dimensions (out of 7) are shown)
A <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
      aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) 

B <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
    aes(color = gestion_5, shape = gestion_5, fill = gestion_5)+ 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 


C <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = periode, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable periode") +
    aes(color = periode, shape = periode, fill = periode) +
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  scale_fill_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d"))

D <- fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen"))


(D+B)/(C+A) 

A <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = quartier, 
             addEllipses = T,
             palette = terrain.colors(10),
             title = "Position des individus dans le plan 1-2 selon la variable quartier") 

B <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = activite, 
             addEllipses = T,
             #palette = terrain.colors(5),
             title = "Position des individus dans le plan 1-2 selon la variable activite") +
aes(color = activite, shape = activite, fill = activite) +
    scale_color_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) +
    scale_fill_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) 


C <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             #palette = terrain.colors(5),
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
    aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

D <- fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen"))

(D+B)/(C+A) 

3.1.1.2 ACP - Classes

Inventaire_Classes_acp <- Inventaire_Classes %>% 
  column_to_rownames(var="Site_gestion_date")
NumVar <- sapply(Inventaire_Classes_acp, is.numeric)
df <- Inventaire_Classes_acp[,NumVar] %>% 
  select(-S_class, -Ab_class)
n <- nrow(df) 
p <- ncol(df)
gestion_2 <- as.factor(Inventaire_Classes_acp$Gestion_2)
gestion_5 <- as.factor(Inventaire_Classes_acp$Gestion_moment_5)
periode <- as.factor(Inventaire_Classes_acp$Periode)
activite <- as.factor(Inventaire_Classes_acp$Activite)
meteo <- as.factor(Inventaire_Classes_acp$Meteo)
site <- as.factor(Inventaire_Classes_acp$Site)
quartier <- as.factor(Inventaire_Classes_acp$Quartier)



acp <- dudi.pca(df, scannf = F, nf = p)
fviz_pca_biplot(acp)

screeplot(acp, main = "Valeurs propres")

fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_var(acp, 
             col.var = "cos2", axes = c(2,3),
             title = "Cercle de corrélation et qualité de représentation dans le plan 2-3", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_ind(acp, repel = T,
             labelsize = 2, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 1-2")

fviz_pca_ind(acp, axes = c(2,3), 
             labelsize = 0, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 2-3")

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
    aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
    aes(color = gestion_5, shape = gestion_5, fill = gestion_5) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = periode, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable periode") +
  aes(color = periode, shape = periode, fill = periode) +
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  scale_fill_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d"))

fviz_pca_ind(acp, col.var = "var",
             labelsize = 0, 
             col.ind = activite, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable activite") + 
  aes(color = activite, shape = activite, fill = activite) +
    scale_color_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) +
    scale_fill_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
  aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = quartier, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable quartier") 

summary(acp)
## Class: pca dudi
## Call: dudi.pca(df = df, scannf = F, nf = p)
## 
## Total inertia: 9
## 
## Eigenvalues:
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##  2.6816  1.2850  1.2256  0.9067  0.8485 
## 
## Projected inertia (%):
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##  29.796  14.278  13.618  10.075   9.428 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   29.80   44.07   57.69   67.77   77.19 
## 
## (Only 5 dimensions (out of 9) are shown)
A <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
    aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f"))  

B <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
    aes(color = gestion_5, shape = gestion_5, fill = gestion_5)+ 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 


C <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = periode, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable periode") +
  aes(color = periode, shape = periode, fill = periode) +
      scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) +
  scale_fill_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d"))

D <- fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen"))


(D+B)/(C+A) 

A <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = quartier, 
             addEllipses = T,
             palette = terrain.colors(10),
             title = "Position des individus dans le plan 1-2 selon la variable quartier") 

B <- fviz_pca_ind(acp,
             labelsize = 0, 
             col.ind = activite, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable activite") + 
  aes(color = activite, shape = activite, fill = activite) +
    scale_color_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) +
    scale_fill_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) 

C <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
  aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

D <- fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen"))

(D+B)/(C+A) 

3.1.1.3 ACP - Fin juillet

AllFinJuillet_acp<- AllFinJuillet[,c(4,19:68)] 
AllFinJuillet_acp <- AllFinJuillet_acp %>% 
  column_to_rownames(var="Parcelle")

NumVar <- sapply(AllFinJuillet_acp, is.numeric)
df <- AllFinJuillet_acp[,NumVar]
n <- nrow(df) 
p <- ncol(df)

gestion_2 <- as.factor(AllFinJuillet$Gestion_2)
gestion_5 <- as.factor(AllFinJuillet$Gestion_moment_5)
activite <- as.factor(AllFinJuillet$Activite)
meteo <- as.factor(AllFinJuillet$Meteo)
site <- as.factor(AllFinJuillet$Site)
quartier <- as.factor(AllFinJuillet$Quartier)

acp <- dudi.pca(df, scannf = F, nf = p)
fviz_pca_biplot(acp)

screeplot(acp, main = "Valeurs propres")

fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_var(acp, 
             col.var = "cos2", axes = c(2,3),
             title = "Cercle de corrélation et qualité de représentation dans le plan 2-3", 
             gradient.cols=c("red", "gold", "forestgreen")) 

fviz_pca_ind(acp, repel = T,
             labelsize = 2, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 1-2")

fviz_pca_ind(acp, axes = c(2,3), 
             labelsize = 0, 
             col.ind = "cos2", 
             gradient.cols=c("red", "gold", "forestgreen"),
             title = "Qualité de représentation des individus dans le plan 2-3")

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
      aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
    aes(color = gestion_5, shape = gestion_5, fill = gestion_5) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 

fviz_pca_ind(acp, col.var = "var",
             labelsize = 0, 
             col.ind = activite, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable activite") + 
  aes(color = activite, shape = activite, fill = activite) +
    scale_color_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) +
    scale_fill_manual(values = c("Forte" = "#3f007d",
                                "Moyenne" = "#6a51a3",
                                "Nulle" = "#9e9ac8")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
  aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = quartier, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable quartier") 

summary(acp)
## Class: pca dudi
## Call: dudi.pca(df = df, scannf = F, nf = p)
## 
## Total inertia: 43
## 
## Eigenvalues:
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##   8.348   4.048   3.627   3.039   2.787 
## 
## Projected inertia (%):
##     Ax1     Ax2     Ax3     Ax4     Ax5 
##  19.413   9.415   8.436   7.067   6.481 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   19.41   28.83   37.26   44.33   50.81 
## 
## (Only 5 dimensions (out of 30) are shown)
A <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_2, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_2") +
    aes(color = gestion_2, shape = gestion_2, fill = gestion_2) + 
    scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
   scale_fill_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f"))  

B <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = gestion_5, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable gestion_5") +
    aes(color = gestion_5, shape = gestion_5, fill = gestion_5)+ 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_fill_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  scale_shape_manual(values = c("Graminées" = 16,
                                  "Fleuri" = 17,
                                  "Semé" = 15,
                                  "Tonte récente" = 5,
                                  "Tonte tardive" = 6)) 

C <- fviz_pca_ind(acp, 
             labelsize = 0, 
             col.ind = meteo, 
             addEllipses = T,
             title = "Position des individus dans le plan 1-2 selon la variable meteo") +
  aes(color = meteo, shape = meteo, fill = meteo) +
    scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
    scale_fill_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) 

D <- fviz_pca_var(acp, 
             col.var = "cos2", 
             title = "Cercle de corrélation et qualité de représentation dans le plan 1-2", 
             gradient.cols=c("red", "gold", "forestgreen"))


(D+B)/(C+A) 

3.1.2 ACC

3.1.2.1 ACC - Sp plantes

Y <- Esp_Plant
X <- Inv %>% 
  select(!c(Site, Parcelle,Gestion_2, Gestion_4, Gestion_moment_4, Date, Heure_debut, Heure_fin, Quartier, Meteo, Jours, Mixte_isole, Activite, Nombre_quadrats))

#dim(X) ; dim(Y) 
X<-X[rowSums(Y)!=0,]
Y<-Y[rowSums(Y)!=0,]
Y<-Y[,colSums(Y)!=0] 
n <- nrow(Y)
#n == nrow(X) 
p <- ncol(Y)
m <- ncol(X)
gestion_2 <- as.factor(X$Gestion_2)
gestion_5 <- as.factor(X$Gestion_moment_5)

afc_Y <- dudi.coa(Y,  scannf = FALSE, nf = p)
acc <- pcaiv(afc_Y, X,  scannf = FALSE, nf = m)

Q <- viz_coaiv(acc,"co") 
viz_coaiv(acc,"li")

viz_coaiv(acc, "li", axes = c(2,3))

Z <-  viz_coaiv(acc, "ls") 
viz_coaiv(acc, "match")

viz_coaiv(acc, "fa") 

E <- viz_coaiv(acc, "cor") 
viz_coaiv(acc, "cor", axes=c(1,3)) 

viz_coaiv(acc, "li", axes = c(1,3))

R <- viz_coaiv(acc, "as") 
viz_coaiv(acc, "as", axes=c(2,3)) 

Z = Z + aes(color = gestion_5) + labs(title = "Position des parcelles") +
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 
Q = Q + labs(title = "Position des espèces de plantes")
E= E + labs(title = "Variables explicatives")
R= R + labs(title = "Participations des axes à la dimention 1-2")

(Q + E) / (Z + R)

3.1.2.2 ACC - Sp poll

Y <- Esp_Poll
X <- Inv %>% 
  select(!c(Site, Parcelle,Gestion_2, Gestion_4, Gestion_moment_4, Date, Heure_debut, Heure_fin, Quartier, Meteo, Jours, Mixte_isole, Activite, Nombre_quadrats))

#dim(X) ; dim(Y) 
X<-X[rowSums(Y)!=0,]
Y<-Y[rowSums(Y)!=0,]
Y<-Y[,colSums(Y)!=0] 
n <- nrow(Y)
#n == nrow(X) 
p <- ncol(Y)
m <- ncol(X)
gestion_2 <- as.factor(X$Gestion_2)
gestion_5 <- as.factor(X$Gestion_moment_5)

afc_Y <- dudi.coa(Y,  scannf = FALSE, nf = p)
acc <- pcaiv(afc_Y, X,  scannf = FALSE, nf = m)

Q <- viz_coaiv(acc,"co") 
viz_coaiv(acc,"li")

viz_coaiv(acc, "li", axes = c(2,3))

Z <-  viz_coaiv(acc, "ls") 
viz_coaiv(acc, "match")

viz_coaiv(acc, "fa") 

E <- viz_coaiv(acc, "cor") 
viz_coaiv(acc, "cor", axes=c(1,3)) 

viz_coaiv(acc, "li", axes = c(1,3))

R <- viz_coaiv(acc, "as") 
viz_coaiv(acc, "as", axes=c(2,3)) 

Z = Z + aes(color = gestion_5) + labs(title = "Position des parcelles") + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 
Q = Q + labs(title = "Position des espèces de pollinisateurs")
E= E + labs(title = "Variables explicatives")
R= R + labs(title = "Participations des axes à la dimention 1-2")

(Q + E) / (Z + R)

3.1.2.3 ACC - Classes poll

Inv_Classes_acp <- Inv_Classes %>% 
  column_to_rownames(var = "Site_gestion_date")
Y <- Inv_Classes_acp
X <- Inv %>% 
  select(!c(Site, Parcelle,Gestion_2, Gestion_4, Gestion_moment_4, Date, Heure_debut, Heure_fin, Quartier, Meteo, Jours, Mixte_isole, Activite, Nombre_quadrats))

#dim(X) ; dim(Y) 
X<-X[rowSums(Y)!=0,]
Y<-Y[rowSums(Y)!=0,]
Y<-Y[,colSums(Y)!=0] 
n <- nrow(Y)
#n == nrow(X) 
p <- ncol(Y)
m <- ncol(X)
gestion_2 <- as.factor(X$Gestion_2)
gestion_5 <- as.factor(X$Gestion_moment_5)

afc_Y <- dudi.coa(Y,  scannf = FALSE, nf = p)
acc <- pcaiv(afc_Y, X,  scannf = FALSE, nf = m)

Q <- viz_coaiv(acc,"co") 
viz_coaiv(acc,"li")

viz_coaiv(acc, "li", axes = c(2,3))

Z <-  viz_coaiv(acc, "ls") 
viz_coaiv(acc, "match")

viz_coaiv(acc, "fa") 

E <- viz_coaiv(acc, "cor") 
viz_coaiv(acc, "cor", axes=c(1,3)) 

viz_coaiv(acc, "li", axes = c(1,3))

R <- viz_coaiv(acc, "as") 
viz_coaiv(acc, "as", axes=c(2,3)) 

Z = Z + aes(color = gestion_5) + labs(title = "Position des parcelles") +
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 
Q = Q + labs(title = "Position des classes de pollinisateurs")
E= E + labs(title = "Variables explicatives")
R= R + labs(title = "Participations des axes à la dimention 1-2")

(Q + E) / (Z + R)

3.1.2.4 ACC - Fin juillet

fin_juil_espPl<- FinJuil_AllPl[,c(1,33:82)] %>% 
  column_to_rownames(var = "ï..Site_gestion_date") 

Y <- fin_juil_espPl
X <- Inv %>% 
  filter(Periode=="Fin juillet") %>% 
  select(!c(Site, Parcelle, Gestion_2, Gestion_4, Gestion_moment_4, Date, Heure_debut, Heure_fin, Jours, Periode, Quartier, Meteo,Jours, Mixte_isole, Activite, Nombre_quadrats))

# dim(X) ; dim(Y) 
X<-X[rowSums(Y)!=0,]
Y<-Y[rowSums(Y)!=0,]
Y<-Y[,colSums(Y)!=0] 
n <- nrow(Y)
# n == nrow(X) 
p <- ncol(Y)
m <- ncol(X)
gestion_2 <- as.factor(X$Gestion_2)
gestion_5 <- as.factor(X$Gestion_moment_5)

afc_Y <- dudi.coa(Y,  scannf = FALSE, nf = p)
acc <- pcaiv(afc_Y, X,  scannf = FALSE, nf = m)

Q <- viz_coaiv(acc,"co") 
viz_coaiv(acc,"li")

viz_coaiv(acc, "li", axes = c(2,3))

Z <-  viz_coaiv(acc, "ls") 
viz_coaiv(acc, "match")

viz_coaiv(acc, "fa") 

E <- viz_coaiv(acc, "cor") 
viz_coaiv(acc, "cor", axes=c(1,3)) 

viz_coaiv(acc, "li", axes = c(1,3))

R <- viz_coaiv(acc, "as") 
viz_coaiv(acc, "as", axes=c(2,3)) 

Z = Z + aes(color = gestion_5) + labs(title = "Position des parcelles") +
      scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 
Q = Q + labs(title = "Position des espèces de plantes")
E= E + labs(title = "Variables explicatives")
R= R + labs(title = "Participations des axes à la dimention 1-2")

(Q + E) / (Z + R)

3.2 Modèles linéaires

3.2.1 LMM 1. G2 - Sélection et simplification des modèles

3.2.1.1 S Plant

LM1_S_Pl <- lmer(S_Plant ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_S_Pl)
# shapiro.test(residuals(LM1_S_Pl))

LM1_S_Pl <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_S_Pl)
# shapiro.test(residuals(LM1_S_Pl))

step(LM1_S_Pl, direction = "backward") # sqrt(S_Plant) ~ Gestion_2 + (1 | Parcelle)
check_model(LM1_S_Pl)

Anova(LM1_S_Pl)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(S_Plant)
##                    Chisq Df Pr(>Chisq)   
## Area_gis_m_sq     0.6372  1   0.424721   
## Temperature       2.7291  1   0.098534 . 
## Periode           4.9930  2   0.082372 . 
## Gestion_2         6.9948  1   0.008175 **
## Periode:Gestion_2 1.4705  2   0.479388   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM1_S_Pl)
# LM1_S_Pl
eLM1_S_Pl_P<-emmeans(LM1_S_Pl,"Periode")
mcLM1_S_Pl_P<-cld(eLM1_S_Pl_P,ajust="tukey")
mcLM1_S_Pl_P$.group<-as.numeric(mcLM1_S_Pl_P$.group)
mcLM1_S_Pl_P$group[mcLM1_S_Pl_P$.group == 1] <- "a"
mcLM1_S_Pl_P$group[mcLM1_S_Pl_P$.group == 2] <- "b"
mcLM1_S_Pl_P
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.87 0.114 68.5     1.64     2.10      1 a    
##  Mi-juillet    1.89 0.111 63.5     1.67     2.11      1 a    
##  Juin          2.05 0.111 63.5     1.83     2.27      1 a    
## 
## Results are averaged over the levels of: Gestion_2 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM1_S_Pl_G2<-emmeans(LM1_S_Pl,"Gestion_2")
mcLM1_S_Pl_G2<-cld(eLM1_S_Pl_G2,ajust="tukey")
mcLM1_S_Pl_G2$.group<-as.numeric(mcLM1_S_Pl_G2$.group)
mcLM1_S_Pl_G2$group[mcLM1_S_Pl_G2$.group == 1] <- "a"
mcLM1_S_Pl_G2$group[mcLM1_S_Pl_G2$.group == 2] <- "b"
mcLM1_S_Pl_G2
##  Gestion_2 emmean    SE   df lower.CL upper.CL .group group
##  Tonte       1.67 0.137 42.4     1.39     1.94      1 a    
##  Fauche      2.21 0.150 43.5     1.90     2.51      2 b    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.1.2 Ab Plant

LM1_Ab_Pl <- lmer(Ab_Plant ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_Ab_Pl)
# shapiro.test(residuals(LM1_Ab_Pl))

LM1_Ab_Pl <- lmer(sqrt(Ab_Plant) ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_Ab_Pl)
# shapiro.test(residuals(LM1_Ab_Pl))

step(LM1_Ab_Pl, direction = "backward") # sqrt(Ab_Plant) ~ Temperature + Periode + (1 | Parcelle)
check_model(LM1_Ab_Pl)

Anova(LM1_Ab_Pl)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(Ab_Plant)
##                     Chisq Df Pr(>Chisq)    
## Area_gis_m_sq      0.0043  1    0.94759    
## Temperature        4.7783  1    0.02882 *  
## Periode           31.3188  2  1.582e-07 ***
## Gestion_2          0.6270  1    0.42847    
## Periode:Gestion_2  3.1873  2    0.20319    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM1_Ab_Pl)
# LM1_Ab_Pl
eLM1_Ab_Pl_P<-emmeans(LM1_Ab_Pl,"Periode")
mcLM1_Ab_Pl_P<-cld(eLM1_Ab_Pl_P,ajust="tukey")
mcLM1_Ab_Pl_P$.group<-as.numeric(mcLM1_Ab_Pl_P$.group)
mcLM1_Ab_Pl_P$group[mcLM1_Ab_Pl_P$.group == 1] <- "b"
mcLM1_Ab_Pl_P$group[mcLM1_Ab_Pl_P$.group == 2] <- "a"
mcLM1_Ab_Pl_P
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   6.50 0.668 84.8     5.17     7.83      1 b    
##  Mi-juillet    7.12 0.645 77.9     5.84     8.41      1 b    
##  Juin          9.69 0.645 77.9     8.41    10.97      2 a    
## 
## Results are averaged over the levels of: Gestion_2 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM1_Ab_Pl_G2<-emmeans(LM1_Ab_Pl,"Gestion_2")
mcLM1_Ab_Pl_G2<-cld(eLM1_Ab_Pl_G2,ajust="tukey")
mcLM1_Ab_Pl_G2$.group<-as.numeric(mcLM1_Ab_Pl_G2$.group)
mcLM1_Ab_Pl_G2$group[mcLM1_Ab_Pl_G2$.group == 1] <- "a"
mcLM1_Ab_Pl_G2$group[mcLM1_Ab_Pl_G2$.group == 2] <- "b"
mcLM1_Ab_Pl_G2
##  Gestion_2 emmean    SE   df lower.CL upper.CL .group group
##  Tonte       7.33 0.742 42.0     5.83     8.82      1 a    
##  Fauche      8.21 0.821 43.7     6.56     9.87      1 a    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.1.3 S Poll

LM1_S_Po <- lmer(S_Poll ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_S_Po)
# shapiro.test(residuals(LM1_S_Po))

LM1_S_Po <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_S_Po)
# shapiro.test(residuals(LM1_S_Po))

step(LM1_S_Po, direction = "backward") # sqrt(S_Poll) ~ Gestion_2 + (1 | Parcelle)
check_model(LM1_S_Po)

Anova(LM1_S_Po)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(S_Poll)
##                     Chisq Df Pr(>Chisq)    
## Area_gis_m_sq      0.3284  1    0.56659    
## Temperature        0.7725  1    0.37945    
## Periode            5.1152  2    0.07749 .  
## Gestion_2         32.0557  1  1.498e-08 ***
## Periode:Gestion_2  0.1999  2    0.90489    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM1_S_Po)
# LM1_S_Po
eLM1_S_Po_P<-emmeans(LM1_S_Po,"Periode")
mcLM1_S_Po_P<-cld(eLM1_S_Po_P,ajust="tukey")
mcLM1_S_Po_P$.group<-as.numeric(mcLM1_S_Po_P$.group)
mcLM1_S_Po_P$group[mcLM1_S_Po_P$.group == 1] <- "a"
mcLM1_S_Po_P$group[mcLM1_S_Po_P$.group == 2] <- "b"
mcLM1_S_Po_P
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   2.01 0.142 97.7     1.72     2.29      1 a    
##  Mi-juillet    2.04 0.136 90.6     1.78     2.31      1 a    
##  Juin          2.30 0.136 90.6     2.03     2.57      1 a    
## 
## Results are averaged over the levels of: Gestion_2 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM1_S_Po_G2<-emmeans(LM1_S_Po,"Gestion_2")
mcLM1_S_Po_G2<-cld(eLM1_S_Po_G2,ajust="tukey")
mcLM1_S_Po_G2$.group<-as.numeric(mcLM1_S_Po_G2$.group)
mcLM1_S_Po_G2$group[mcLM1_S_Po_G2$.group == 1] <- "a"
mcLM1_S_Po_G2$group[mcLM1_S_Po_G2$.group == 2] <- "b"
mcLM1_S_Po_G2
##  Gestion_2 emmean    SE   df lower.CL upper.CL .group group
##  Tonte       1.48 0.147 41.6     1.19     1.78      1 a    
##  Fauche      2.75 0.164 43.9     2.42     3.08      2 b    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.1.4 Ab Poll

LM1_Ab_Po <- lmer(Ab_Poll ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_Ab_Po)
# shapiro.test(residuals(LM1_Ab_Po))

LM1_Ab_Po <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode * Gestion_2 + (1|Parcelle), data = Inv)
# check_model(LM1_Ab_Po)
# shapiro.test(residuals(LM1_Ab_Po))

step(LM1_Ab_Po, direction = "backward") # sqrt(Ab_Poll) ~ Gestion_2 + (1 | Parcelle)
check_model(LM1_Ab_Po)

Anova(LM1_Ab_Po)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(Ab_Poll)
##                     Chisq Df Pr(>Chisq)    
## Area_gis_m_sq      0.0360  1     0.8496    
## Temperature        0.0007  1     0.9791    
## Periode            3.3492  2     0.1874    
## Gestion_2         28.7659  1  8.168e-08 ***
## Periode:Gestion_2  2.9264  2     0.2315    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM1_Ab_Po)
# LM1_Ab_Po
eLM1_Ab_Po_P<-emmeans(LM1_Ab_Po,"Periode")
mcLM1_Ab_Po_P<-cld(eLM1_Ab_Po_P,ajust="tukey")
mcLM1_Ab_Po_P$.group<-as.numeric(mcLM1_Ab_Po_P$.group)
mcLM1_Ab_Po_P$group[mcLM1_Ab_Po_P$.group == 1] <- "a"
mcLM1_Ab_Po_P$group[mcLM1_Ab_Po_P$.group == 2] <- "b"
mcLM1_Ab_Po_P
##  Periode     emmean    SE    df lower.CL upper.CL .group group
##  Fin juillet   2.81 0.254 100.8     2.30     3.31      1 a    
##  Mi-juillet    3.18 0.243  93.8     2.69     3.66      1 a    
##  Juin          3.30 0.243  93.9     2.82     3.78      1 a    
## 
## Results are averaged over the levels of: Gestion_2 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM1_Ab_Po_G2<-emmeans(LM1_Ab_Po,"Gestion_2")
mcLM1_Ab_Po_G2<-cld(eLM1_Ab_Po_G2,ajust="tukey")
mcLM1_Ab_Po_G2$.group<-as.numeric(mcLM1_Ab_Po_G2$.group)
mcLM1_Ab_Po_G2$group[mcLM1_Ab_Po_G2$.group == 1] <- "a"
mcLM1_Ab_Po_G2$group[mcLM1_Ab_Po_G2$.group == 2] <- "b"
mcLM1_Ab_Po_G2
##  Gestion_2 emmean    SE   df lower.CL upper.CL .group group
##  Tonte       2.04 0.259 41.5     1.52     2.57      1 a    
##  Fauche      4.14 0.289 44.0     3.56     4.73      2 b    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.2 LMM 2. G5 - Sélection et simplification des modèles

3.2.2.1 S Plant

LM2_S_Pl <- lmer(S_Plant ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_S_Pl)
# shapiro.test(residuals(LM2_S_Pl))

LM2_S_Pl <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_S_Pl)
# shapiro.test(residuals(LM2_S_Pl))

step(LM2_S_Pl, direction = "backward") # sqrt(S_Plant) ~ Periode + Gestion_moment_5 + (1 | Parcelle)
check_model(LM2_S_Pl)

Anova(LM2_S_Pl)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(S_Plant)
##                            Chisq Df Pr(>Chisq)    
## Area_gis_m_sq             0.1796  1     0.6717    
## Temperature               1.1648  1     0.2805    
## Periode                   7.6610  2     0.0217 *  
## Gestion_moment_5         74.9761  4  2.016e-15 ***
## Periode:Gestion_moment_5 11.2378  8     0.1886    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM2_S_Pl)
# LM2_S_Pl
eLM2_S_Pl_P<-emmeans(LM2_S_Pl,"Periode")
mcLM2_S_Pl_P<-cld(eLM2_S_Pl_P,ajust="tukey")
mcLM2_S_Pl_P$.group<-as.numeric(mcLM2_S_Pl_P$.group)
mcLM2_S_Pl_P$group[mcLM2_S_Pl_P$.group == 1] <- "a"
mcLM2_S_Pl_P$group[mcLM2_S_Pl_P$.group == 2] <- "b"
mcLM2_S_Pl_P
##  Periode     emmean     SE   df lower.CL upper.CL .group group
##  Fin juillet   1.84 0.1008 82.7     1.64     2.04      1 a    
##  Mi-juillet    1.85 0.0930 67.9     1.67     2.04      1 a    
##  Juin          1.98 0.0939 69.7     1.79     2.17      1 a    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Pl_P_G <- Inv %>% 
  filter(Gestion_moment_5 == "Graminées")
LM2_S_Pl_P_G <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Pl_P_G)
eLM2_S_Pl_P_G<-emmeans(LM2_S_Pl_P_G,"Periode")
mcLM2_S_Pl_P_G<-cld(eLM2_S_Pl_P_G,ajust="tukey")
mcLM2_S_Pl_P_G$.group<-as.numeric(mcLM2_S_Pl_P_G$.group)
mcLM2_S_Pl_P_G$group[mcLM2_S_Pl_P_G$.group == 1] <- "a"
mcLM2_S_Pl_P_G$group[mcLM2_S_Pl_P_G$.group == 2] <- "b"
mcLM2_S_Pl_P_G
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet    1.05 0.384 2.71   -0.254     2.35      1 a    
##  Juin          1.07 0.387 2.76   -0.221     2.37      1 a    
##  Fin juillet   1.37 0.399 3.08    0.119     2.62      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Pl_P_F <- Inv %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM2_S_Pl_P_F <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Pl_P_F)
eLM2_S_Pl_P_F<-emmeans(LM2_S_Pl_P_F,"Periode")
mcLM2_S_Pl_P_F<-cld(eLM2_S_Pl_P_F,ajust="tukey")
mcLM2_S_Pl_P_F$.group<-as.numeric(mcLM2_S_Pl_P_F$.group)
mcLM2_S_Pl_P_F$group[mcLM2_S_Pl_P_F$.group == 1] <- "a"
mcLM2_S_Pl_P_F$group[mcLM2_S_Pl_P_F$.group == 2] <- "b"
mcLM2_S_Pl_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.93 0.186 14.5     1.53     2.33      1 a    
##  Mi-juillet    2.31 0.175 12.1     1.92     2.69      2 b    
##  Juin          2.47 0.175 12.1     2.08     2.85      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Pl_P_S <- Inv %>% 
  filter(Gestion_moment_5 == "Semé")
LM2_S_Pl_P_S <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Pl_P_S)
eLM2_S_Pl_P_S<-emmeans(LM2_S_Pl_P_S,"Periode")
mcLM2_S_Pl_P_S<-cld(eLM2_S_Pl_P_S,ajust="tukey")
mcLM2_S_Pl_P_S$.group<-as.numeric(mcLM2_S_Pl_P_S$.group)
mcLM2_S_Pl_P_S$group[mcLM2_S_Pl_P_S$.group == 1] <- "a"
mcLM2_S_Pl_P_S$group[mcLM2_S_Pl_P_S$.group == 2] <- "b"
mcLM2_S_Pl_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   2.80 0.183 8.58     2.39     3.22      1 a    
##  Mi-juillet    2.97 0.178 8.19     2.56     3.38      1 a    
##  Juin          3.06 0.177 8.12     2.65     3.47      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Pl_P_Tr <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM2_S_Pl_P_Tr <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Pl_P_Tr)
eLM2_S_Pl_P_Tr<-emmeans(LM2_S_Pl_P_Tr,"Periode")
mcLM2_S_Pl_P_Tr<-cld(eLM2_S_Pl_P_Tr,ajust="tukey")
mcLM2_S_Pl_P_Tr$.group<-as.numeric(mcLM2_S_Pl_P_Tr$.group)
mcLM2_S_Pl_P_Tr$group[mcLM2_S_Pl_P_Tr$.group == 1] <- "a"
mcLM2_S_Pl_P_Tr$group[mcLM2_S_Pl_P_Tr$.group == 2] <- "b"
mcLM2_S_Pl_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.18 0.245 19.8    0.664     1.69      1 a    
##  Mi-juillet    1.22 0.136 21.0    0.939     1.50      1 a    
##  Juin          1.54 0.182 20.9    1.158     1.92      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Pl_P_Tt <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM2_S_Pl_P_Tt <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Pl_P_Tt)
eLM2_S_Pl_P_Tt<-emmeans(LM2_S_Pl_P_Tt,"Periode")
mcLM2_S_Pl_P_Tt<-cld(eLM2_S_Pl_P_Tt,ajust="tukey")
mcLM2_S_Pl_P_Tt$.group<-as.numeric(mcLM2_S_Pl_P_Tt$.group)
mcLM2_S_Pl_P_Tt$group[mcLM2_S_Pl_P_Tt$.group == 1] <- "a"
mcLM2_S_Pl_P_Tt$group[mcLM2_S_Pl_P_Tt$.group == 2] <- "b"
mcLM2_S_Pl_P_Tt
##  Periode     emmean     SE   df lower.CL upper.CL .group group
##  Fin juillet   1.72 0.0882 43.2     1.54     1.90      1 a    
##  Juin          1.90 0.0992 43.4     1.70     2.10      1 a    
##  Mi-juillet    1.91 0.1122 44.0     1.69     2.14      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM2_S_Pl_G5<-emmeans(LM2_S_Pl,"Gestion_moment_5")
mcLM2_S_Pl_G5<-cld(eLM2_S_Pl_G5,ajust="tukey")
mcLM2_S_Pl_G5$.group<-as.numeric(mcLM2_S_Pl_G5$.group)
mcLM2_S_Pl_G5$group[mcLM2_S_Pl_G5$.group == 1] <- "a"
mcLM2_S_Pl_G5$group[mcLM2_S_Pl_G5$.group == 2] <- "b"
mcLM2_S_Pl_G5$group[mcLM2_S_Pl_G5$.group == 3] <- "c"
mcLM2_S_Pl_G5$group[mcLM2_S_Pl_G5$.group == 23] <- "bc"
mcLM2_S_Pl_G5
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Graminées          1.06 0.249 41.8    0.563     1.57      1 a    
##  Tonte récente      1.37 0.123 77.4    1.127     1.62      1 a    
##  Tonte tardive      1.84 0.106 49.6    1.627     2.05      2 b    
##  Fleuri             2.24 0.152 42.0    1.937     2.55     23 bc   
##  Semé               2.93 0.203 39.7    2.516     3.34      3 c    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.2.2 Ab Plant

LM2_Ab_Pl <- lmer(Ab_Plant ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_Ab_Pl)
# shapiro.test(residuals(LM2_Ab_Pl))

LM2_Ab_Pl <- lmer(sqrt(Ab_Plant) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_Ab_Pl)
# shapiro.test(residuals(LM2_Ab_Pl))

step(LM2_Ab_Pl, direction = "backward") # sqrt(Ab_Plant) ~ Temperature + Periode + Gestion_moment_5 + (1 | Parcelle)
check_model(LM2_Ab_Pl)

Anova(LM2_Ab_Pl)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(Ab_Plant)
##                            Chisq Df Pr(>Chisq)    
## Area_gis_m_sq             0.2566  1     0.6125    
## Temperature               1.8593  1     0.1727    
## Periode                  41.7713  2  8.501e-10 ***
## Gestion_moment_5         44.9290  4  4.113e-09 ***
## Periode:Gestion_moment_5  7.1898  8     0.5163    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM2_Ab_Pl)
# LM2_Ab_Pl
eLM2_Ab_Pl_P<-emmeans(LM2_Ab_Pl,"Periode")
mcLM2_Ab_Pl_P<-cld(eLM2_Ab_Pl_P,ajust="tukey")
mcLM2_Ab_Pl_P$.group<-as.numeric(mcLM2_Ab_Pl_P$.group)
mcLM2_Ab_Pl_P$group[mcLM2_Ab_Pl_P$.group == 1] <- "b"
mcLM2_Ab_Pl_P$group[mcLM2_Ab_Pl_P$.group == 2] <- "a"
mcLM2_Ab_Pl_P
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   6.07 0.670 92.8     4.74     7.40      1 b    
##  Mi-juillet    6.94 0.609 77.0     5.72     8.15      1 b    
##  Juin          8.78 0.616 79.0     7.55    10.00      2 a    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Pl_P_G <- Inv %>% 
  filter(Gestion_moment_5 == "Graminées")
LM2_Ab_Pl_P_G <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_Ab_Pl_P_G)
eLM2_Ab_Pl_P_G<-emmeans(LM2_Ab_Pl_P_G,"Periode")
mcLM2_Ab_Pl_P_G<-cld(eLM2_Ab_Pl_P_G,ajust="tukey")
mcLM2_Ab_Pl_P_G$.group<-as.numeric(mcLM2_Ab_Pl_P_G$.group)
mcLM2_Ab_Pl_P_G$group[mcLM2_Ab_Pl_P_G$.group == 1] <- "a"
mcLM2_Ab_Pl_P_G$group[mcLM2_Ab_Pl_P_G$.group == 2] <- "b"
mcLM2_Ab_Pl_P_G
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet    1.05 0.384 2.71   -0.254     2.35      1 a    
##  Juin          1.07 0.387 2.76   -0.221     2.37      1 a    
##  Fin juillet   1.37 0.399 3.08    0.119     2.62      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Pl_P_F <- Inv %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM2_Ab_Pl_P_F <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_Ab_Pl_P_F)
eLM2_Ab_Pl_P_F<-emmeans(LM2_Ab_Pl_P_F,"Periode")
mcLM2_Ab_Pl_P_F<-cld(eLM2_Ab_Pl_P_F,ajust="tukey")
mcLM2_Ab_Pl_P_F$.group<-as.numeric(mcLM2_Ab_Pl_P_F$.group)
mcLM2_Ab_Pl_P_F$group[mcLM2_Ab_Pl_P_F$.group == 1] <- "a"
mcLM2_Ab_Pl_P_F$group[mcLM2_Ab_Pl_P_F$.group == 2] <- "b"
mcLM2_Ab_Pl_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.93 0.186 14.5     1.53     2.33      1 a    
##  Mi-juillet    2.31 0.175 12.1     1.92     2.69      2 b    
##  Juin          2.47 0.175 12.1     2.08     2.85      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Pl_P_S <- Inv %>% 
  filter(Gestion_moment_5 == "Semé")
LM2_Ab_Pl_P_S <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_Ab_Pl_P_S)
eLM2_Ab_Pl_P_S<-emmeans(LM2_Ab_Pl_P_S,"Periode")
mcLM2_Ab_Pl_P_S<-cld(eLM2_Ab_Pl_P_S,ajust="tukey")
mcLM2_Ab_Pl_P_S$.group<-as.numeric(mcLM2_Ab_Pl_P_S$.group)
mcLM2_Ab_Pl_P_S$group[mcLM2_Ab_Pl_P_S$.group == 1] <- "a"
mcLM2_Ab_Pl_P_S$group[mcLM2_Ab_Pl_P_S$.group == 2] <- "b"
mcLM2_Ab_Pl_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   2.80 0.183 8.58     2.39     3.22      1 a    
##  Mi-juillet    2.97 0.178 8.19     2.56     3.38      1 a    
##  Juin          3.06 0.177 8.12     2.65     3.47      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Pl_P_Tr <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM2_Ab_Pl_P_Tr <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_Ab_Pl_P_Tr)
eLM2_Ab_Pl_P_Tr<-emmeans(LM2_Ab_Pl_P_Tr,"Periode")
mcLM2_Ab_Pl_P_Tr<-cld(eLM2_Ab_Pl_P_Tr,ajust="tukey")
mcLM2_Ab_Pl_P_Tr$.group<-as.numeric(mcLM2_Ab_Pl_P_Tr$.group)
mcLM2_Ab_Pl_P_Tr$group[mcLM2_Ab_Pl_P_Tr$.group == 1] <- "a"
mcLM2_Ab_Pl_P_Tr$group[mcLM2_Ab_Pl_P_Tr$.group == 2] <- "b"
mcLM2_Ab_Pl_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.18 0.245 19.8    0.664     1.69      1 a    
##  Mi-juillet    1.22 0.136 21.0    0.939     1.50      1 a    
##  Juin          1.54 0.182 20.9    1.158     1.92      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Pl_P_Tt <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM2_Ab_Pl_P_Tt <- lmer(sqrt(S_Plant) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_Ab_Pl_P_Tt)
eLM2_Ab_Pl_P_Tt<-emmeans(LM2_Ab_Pl_P_Tt,"Periode")
mcLM2_Ab_Pl_P_Tt<-cld(eLM2_Ab_Pl_P_Tt,ajust="tukey")
mcLM2_Ab_Pl_P_Tt$.group<-as.numeric(mcLM2_Ab_Pl_P_Tt$.group)
mcLM2_Ab_Pl_P_Tt$group[mcLM2_Ab_Pl_P_Tt$.group == 1] <- "a"
mcLM2_Ab_Pl_P_Tt$group[mcLM2_Ab_Pl_P_Tt$.group == 2] <- "b"
mcLM2_Ab_Pl_P_Tt
##  Periode     emmean     SE   df lower.CL upper.CL .group group
##  Fin juillet   1.72 0.0882 43.2     1.54     1.90      1 a    
##  Juin          1.90 0.0992 43.4     1.70     2.10      1 a    
##  Mi-juillet    1.91 0.1122 44.0     1.69     2.14      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM2_Ab_Pl_G5<-emmeans(LM2_Ab_Pl,"Gestion_moment_5")
mcLM2_Ab_Pl_G5<-cld(eLM2_Ab_Pl_G5,ajust="tukey")
mcLM2_Ab_Pl_G5$.group<-as.numeric(mcLM2_Ab_Pl_G5$.group)
mcLM2_Ab_Pl_G5$group[mcLM2_Ab_Pl_G5$.group == 1] <- "a"
mcLM2_Ab_Pl_G5$group[mcLM2_Ab_Pl_G5$.group == 2] <- "b"
mcLM2_Ab_Pl_G5$group[mcLM2_Ab_Pl_G5$.group == 3] <- "c"
mcLM2_Ab_Pl_G5$group[mcLM2_Ab_Pl_G5$.group == 23] <- "bc"
mcLM2_Ab_Pl_G5
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Graminées          2.98 1.564 42.0   -0.174     6.14      1 a    
##  Tonte récente      5.11 0.816 85.1    3.483     6.73      1 a    
##  Tonte tardive      8.60 0.675 52.3    7.246     9.96      2 b    
##  Fleuri             8.72 0.956 42.4    6.793    10.65      2 b    
##  Semé              10.89 1.269 39.4    8.323    13.45      2 b    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.2.3 S Poll

LM2_S_Po <- lmer(S_Poll ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_S_Po)
# shapiro.test(residuals(LM2_S_Po))

LM2_S_Po <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_S_Po)
# shapiro.test(residuals(LM2_S_Po))

step(LM2_S_Po, direction = "backward") 
# sqrt(S_Poll) ~ Periode + Gestion_moment_5 + (1 | Parcelle) + Periode:Gestion_moment_5
check_model(LM2_S_Po)

Anova(LM2_S_Po)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(S_Poll)
##                            Chisq Df Pr(>Chisq)    
## Area_gis_m_sq             0.0491  1    0.82467    
## Temperature               0.3952  1    0.52957    
## Periode                   7.6092  2    0.02227 *  
## Gestion_moment_5         93.2565  4    < 2e-16 ***
## Periode:Gestion_moment_5 18.1967  8    0.01980 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM2_S_Po)
# LM2_S_Po
eLM2_S_Po_P<-emmeans(LM2_S_Po,"Periode")
mcLM2_S_Po_P<-cld(eLM2_S_Po_P,ajust="tukey")
mcLM2_S_Po_P$.group<-as.numeric(mcLM2_S_Po_P$.group)
mcLM2_S_Po_P$group[mcLM2_S_Po_P$.group == 1] <- "a"
mcLM2_S_Po_P$group[mcLM2_S_Po_P$.group == 2] <- "b"
mcLM2_S_Po_P
##  Periode     emmean    SE    df lower.CL upper.CL .group group
##  Mi-juillet    2.08 0.123  94.0     1.84     2.33      1 a    
##  Fin juillet   2.11 0.139 106.5     1.83     2.38      1 a    
##  Juin          2.24 0.125  95.8     1.99     2.48      1 a    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_P_G <- Inv %>% 
  filter(Gestion_moment_5 == "Graminées")
LM2_S_Po_P_G <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Po_P_G)
eLM2_S_Po_P_G<-emmeans(LM2_S_Po_P_G,"Periode")
mcLM2_S_Po_P_G<-cld(eLM2_S_Po_P_G,ajust="tukey")
mcLM2_S_Po_P_G$.group<-as.numeric(mcLM2_S_Po_P_G$.group)
mcLM2_S_Po_P_G$group[mcLM2_S_Po_P_G$.group == 1] <- "a"
mcLM2_S_Po_P_G$group[mcLM2_S_Po_P_G$.group == 2] <- "b"
mcLM2_S_Po_P_G
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin          1.15 0.638 5.85  -0.4187     2.72      1 a    
##  Mi-juillet    1.58 0.619 5.87   0.0596     3.10      1 a    
##  Fin juillet   2.46 0.711 5.99   0.7142     4.20      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_P_F <- Inv %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM2_S_Po_P_F <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Po_P_F)
eLM2_S_Po_P_F<-emmeans(LM2_S_Po_P_F,"Periode")
mcLM2_S_Po_P_F<-cld(eLM2_S_Po_P_F,ajust="tukey")
mcLM2_S_Po_P_F$.group<-as.numeric(mcLM2_S_Po_P_F$.group)
mcLM2_S_Po_P_F$group[mcLM2_S_Po_P_F$.group == 1] <- "a"
mcLM2_S_Po_P_F$group[mcLM2_S_Po_P_F$.group == 2] <- "b"
mcLM2_S_Po_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   2.60 0.255 20.0     2.07     3.13      1 a    
##  Mi-juillet    2.81 0.227 16.4     2.33     3.29      1 a    
##  Juin          2.98 0.228 16.5     2.50     3.47      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_P_S <- Inv %>% 
  filter(Gestion_moment_5 == "Semé")
LM2_S_Po_P_S <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Po_P_S)
eLM2_S_Po_P_S<-emmeans(LM2_S_Po_P_S,"Periode")
mcLM2_S_Po_P_S<-cld(eLM2_S_Po_P_S,ajust="tukey")
mcLM2_S_Po_P_S$.group<-as.numeric(mcLM2_S_Po_P_S$.group)
mcLM2_S_Po_P_S$group[mcLM2_S_Po_P_S$.group == 1] <- "a"
mcLM2_S_Po_P_S$group[mcLM2_S_Po_P_S$.group == 2] <- "b"
mcLM2_S_Po_P_S$group[mcLM2_S_Po_P_S$.group == 12] <- "ab"
mcLM2_S_Po_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   2.97 0.208 11.3     2.51     3.43      1 a    
##  Mi-juillet    3.52 0.201 11.0     3.07     3.96     12 ab   
##  Juin          3.78 0.199 11.0     3.34     4.21      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_P_Tr <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM2_S_Po_P_Tr <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Po_P_Tr)
eLM2_S_Po_P_Tr<-emmeans(LM2_S_Po_P_Tr,"Periode")
mcLM2_S_Po_P_Tr<-cld(eLM2_S_Po_P_Tr,ajust="tukey")
mcLM2_S_Po_P_Tr$.group<-as.numeric(mcLM2_S_Po_P_Tr$.group)
mcLM2_S_Po_P_Tr$group[mcLM2_S_Po_P_Tr$.group == 1] <- "a"
mcLM2_S_Po_P_Tr$group[mcLM2_S_Po_P_Tr$.group == 2] <- "b"
mcLM2_S_Po_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet   0.874 0.182 21.0    0.495     1.25      1 a    
##  Fin juillet  1.060 0.329 19.8    0.373     1.75      1 a    
##  Juin         1.407 0.244 20.9    0.898     1.91      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_P_Tt <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM2_S_Po_P_Tt <- lmer(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Po_P_Tt)
eLM2_S_Po_P_Tt<-emmeans(LM2_S_Po_P_Tt,"Periode")
mcLM2_S_Po_P_Tt<-cld(eLM2_S_Po_P_Tt,ajust="tukey")
mcLM2_S_Po_P_Tt$.group<-as.numeric(mcLM2_S_Po_P_Tt$.group)
mcLM2_S_Po_P_Tt$group[mcLM2_S_Po_P_Tt$.group == 1] <- "a"
mcLM2_S_Po_P_Tt$group[mcLM2_S_Po_P_Tt$.group == 2] <- "b"
mcLM2_S_Po_P_Tt$group[mcLM2_S_Po_P_Tt$.group == 12] <- "ab"
mcLM2_S_Po_P_Tt
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.37 0.143 44.0     1.08     1.66      1 a    
##  Mi-juillet    1.87 0.185 44.0     1.50     2.24     12 ab   
##  Juin          1.92 0.161 43.7     1.60     2.25      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM2_S_Po_G5<-emmeans(LM2_S_Po,"Gestion_moment_5")
mcLM2_S_Po_G5<-cld(eLM2_S_Po_G5,ajust="tukey")
mcLM2_S_Po_G5$.group<-as.numeric(mcLM2_S_Po_G5$.group)
mcLM2_S_Po_G5$group[mcLM2_S_Po_G5$.group == 1] <- "a"
mcLM2_S_Po_G5$group[mcLM2_S_Po_G5$.group == 2] <- "b"
mcLM2_S_Po_G5$group[mcLM2_S_Po_G5$.group == 3] <- "c"
mcLM2_S_Po_G5$group[mcLM2_S_Po_G5$.group == 12] <- "ab"
mcLM2_S_Po_G5
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte récente      1.09 0.167 95.1    0.759     1.42      1 a    
##  Graminées          1.67 0.292 42.3    1.079     2.26     12 ab   
##  Tonte tardive      1.72 0.130 57.4    1.455     1.98      2 b    
##  Fleuri             2.83 0.179 43.0    2.466     3.19      3 c    
##  Semé               3.41 0.235 38.6    2.932     3.88      3 c    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_G5_Ju <- Inv %>% 
  filter(Periode == "Juin")
LM2_S_Po_G5_Ju <- lm(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_S_Po_G5_Ju)
eLM2_S_Po_G5_Ju<-emmeans(LM2_S_Po_G5_Ju,"Gestion_moment_5")
mcLM2_S_Po_G5_Ju<-cld(eLM2_S_Po_G5_Ju,ajust="tukey")
mcLM2_S_Po_G5_Ju$.group<-as.numeric(mcLM2_S_Po_G5_Ju$.group)
mcLM2_S_Po_G5_Ju$group[mcLM2_S_Po_G5_Ju$.group == 1] <- "a"
mcLM2_S_Po_G5_Ju$group[mcLM2_S_Po_G5_Ju$.group == 2] <- "b"
mcLM2_S_Po_G5_Ju
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Graminées          1.13 0.317 39    0.488     1.77      1 a    
##  Tonte récente      1.33 0.232 39    0.860     1.80      1 a    
##  Tonte tardive      1.92 0.163 39    1.593     2.25      1 a    
##  Fleuri             3.06 0.194 39    2.664     3.45      2 b    
##  Semé               3.75 0.262 39    3.215     4.28      2 b    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_G5_mJ <- Inv %>% 
  filter(Periode == "Mi-juillet")
LM2_S_Po_G5_mJ <- lm(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_S_Po_G5_mJ)
eLM2_S_Po_G5_mJ<-emmeans(LM2_S_Po_G5_mJ,"Gestion_moment_5")
mcLM2_S_Po_G5_mJ<-cld(eLM2_S_Po_G5_mJ,ajust="tukey")
mcLM2_S_Po_G5_mJ$.group<-as.numeric(mcLM2_S_Po_G5_mJ$.group)
mcLM2_S_Po_G5_mJ$group[mcLM2_S_Po_G5_mJ$.group == 1] <- "a"
mcLM2_S_Po_G5_mJ$group[mcLM2_S_Po_G5_mJ$.group == 12] <- "ab"
mcLM2_S_Po_G5_mJ$group[mcLM2_S_Po_G5_mJ$.group == 23] <- "bc"
mcLM2_S_Po_G5_mJ$group[mcLM2_S_Po_G5_mJ$.group == 34] <- "cd"
mcLM2_S_Po_G5_mJ$group[mcLM2_S_Po_G5_mJ$.group == 4] <- "d"
mcLM2_S_Po_G5_mJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente     0.888 0.228 39    0.426     1.35      1 a    
##  Graminées         1.149 0.422 39    0.294     2.00     12 ab   
##  Tonte tardive     1.948 0.245 39    1.453     2.44     23 bc   
##  Fleuri            2.807 0.255 39    2.292     3.32     34 cd   
##  Semé              3.449 0.341 39    2.760     4.14      4 d    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Po_G5_fJ <- Inv %>% 
  filter(Periode == "Fin juillet")
LM2_S_Po_G5_fJ <- lm(sqrt(S_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_S_Po_G5_fJ)
eLM2_S_Po_G5_fJ<-emmeans(LM2_S_Po_G5_fJ,"Gestion_moment_5")
mcLM2_S_Po_G5_fJ<-cld(eLM2_S_Po_G5_fJ,ajust="tukey")
mcLM2_S_Po_G5_fJ$.group<-as.numeric(mcLM2_S_Po_G5_fJ$.group)
mcLM2_S_Po_G5_fJ$group[mcLM2_S_Po_G5_fJ$.group == 1] <- "a"
mcLM2_S_Po_G5_fJ$group[mcLM2_S_Po_G5_fJ$.group == 2] <- "b"
mcLM2_S_Po_G5_fJ$group[mcLM2_S_Po_G5_fJ$.group == 12] <- "ab"
mcLM2_S_Po_G5_fJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente      0.99 0.333 35    0.314     1.67      1 a    
##  Tonte tardive      1.37 0.164 35    1.042     1.71      1 a    
##  Fleuri             2.61 0.261 35    2.077     3.14      2 b    
##  Graminées          2.81 0.420 35    1.957     3.66      2 b    
##  Semé               3.06 0.308 35    2.434     3.69      2 b    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.2.4 Ab Poll

LM2_Ab_Po <- lmer(Ab_Poll ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_Ab_Po)
# shapiro.test(residuals(LM2_Ab_Po))

LM2_Ab_Po <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Inv)
# check_model(LM2_Ab_Po)
# shapiro.test(residuals(LM2_Ab_Po))

step(LM2_Ab_Po, direction = "backward") # sqrt(Ab_Poll) ~ Periode + Gestion_moment_5 + (1 | Parcelle) + Periode:Gestion_moment_5
check_model(LM2_Ab_Po)

Anova(LM2_Ab_Po)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(Ab_Poll)
##                             Chisq Df Pr(>Chisq)    
## Area_gis_m_sq              0.1000  1  0.7518512    
## Temperature                0.5316  1  0.4659370    
## Periode                    9.6825  2  0.0078974 ** 
## Gestion_moment_5         116.0088  4  < 2.2e-16 ***
## Periode:Gestion_moment_5  29.0253  8  0.0003139 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# summary(LM2_Ab_Po)
# LM2_Ab_Po
eLM2_Ab_Po_P<-emmeans(LM2_Ab_Po,"Periode")
mcLM2_Ab_Po_P<-cld(eLM2_Ab_Po_P,ajust="tukey")
mcLM2_Ab_Po_P$.group<-as.numeric(mcLM2_Ab_Po_P$.group)
mcLM2_Ab_Po_P$group[mcLM2_Ab_Po_P$.group == 1] <- "a"
mcLM2_Ab_Po_P$group[mcLM2_Ab_Po_P$.group == 2] <- "b"
mcLM2_Ab_Po_P
##  Periode     emmean    SE    df lower.CL upper.CL .group group
##  Fin juillet   3.01 0.232 108.4     2.55     3.47      1 a    
##  Juin          3.13 0.208  98.8     2.72     3.54      1 a    
##  Mi-juillet    3.35 0.205  97.2     2.94     3.76      1 a    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Ab_P_G <- Inv %>% 
  filter(Gestion_moment_5 == "Graminées")
LM2_S_Ab_P_G <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Ab_P_G)
eLM2_S_Ab_P_G<-emmeans(LM2_S_Ab_P_G,"Periode")
mcLM2_S_Ab_P_G<-cld(eLM2_S_Ab_P_G,ajust="tukey")
mcLM2_S_Ab_P_G$.group<-as.numeric(mcLM2_S_Ab_P_G$.group)
mcLM2_S_Ab_P_G$group[mcLM2_S_Ab_P_G$.group == 1] <- "a"
mcLM2_S_Ab_P_G$group[mcLM2_S_Ab_P_G$.group == 2] <- "b"
mcLM2_S_Ab_P_G
##  Periode     emmean   SE   df lower.CL upper.CL .group group
##  Juin          1.13 1.06 5.85   -1.475     3.73      1 a    
##  Mi-juillet    2.37 1.03 5.87   -0.157     4.89      1 a    
##  Fin juillet   3.68 1.18 5.99    0.797     6.57      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Ab_P_F <- Inv %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM2_S_Ab_P_F <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Ab_P_F)
eLM2_S_Ab_P_F<-emmeans(LM2_S_Ab_P_F,"Periode")
mcLM2_S_Ab_P_F<-cld(eLM2_S_Ab_P_F,ajust="tukey")
mcLM2_S_Ab_P_F$.group<-as.numeric(mcLM2_S_Ab_P_F$.group)
mcLM2_S_Ab_P_F$group[mcLM2_S_Ab_P_F$.group == 1] <- "a"
mcLM2_S_Ab_P_F$group[mcLM2_S_Ab_P_F$.group == 2] <- "b"
mcLM2_S_Ab_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   3.33 0.471 18.5     2.34     4.31      1 a    
##  Mi-juillet    4.14 0.427 15.0     3.23     5.05      1 a    
##  Juin          4.43 0.429 15.2     3.52     5.34      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Ab_P_S <- Inv %>% 
  filter(Gestion_moment_5 == "Semé")
LM2_S_Ab_P_S <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Ab_P_S)
eLM2_S_Ab_P_S<-emmeans(LM2_S_Ab_P_S,"Periode")
mcLM2_S_Ab_P_S<-cld(eLM2_S_Ab_P_S,ajust="tukey")
mcLM2_S_Ab_P_S$.group<-as.numeric(mcLM2_S_Ab_P_S$.group)
mcLM2_S_Ab_P_S$group[mcLM2_S_Ab_P_S$.group == 1] <- "a"
mcLM2_S_Ab_P_S$group[mcLM2_S_Ab_P_S$.group == 2] <- "b"
mcLM2_S_Ab_P_S$group[mcLM2_S_Ab_P_S$.group == 12] <- "ab"
mcLM2_S_Ab_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   4.73 0.384 11.1     3.88     5.57      1 a    
##  Juin          5.43 0.369 10.8     4.61     6.24      1 a    
##  Mi-juillet    6.95 0.371 10.8     6.13     7.76      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Ab_P_Tr <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM2_S_Ab_P_Tr <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Ab_P_Tr)
eLM2_S_Ab_P_Tr<-emmeans(LM2_S_Ab_P_Tr,"Periode")
mcLM2_S_Ab_P_Tr<-cld(eLM2_S_Ab_P_Tr,ajust="tukey")
mcLM2_S_Ab_P_Tr$.group<-as.numeric(mcLM2_S_Ab_P_Tr$.group)
mcLM2_S_Ab_P_Tr$group[mcLM2_S_Ab_P_Tr$.group == 1] <- "a"
mcLM2_S_Ab_P_Tr$group[mcLM2_S_Ab_P_Tr$.group == 2] <- "b"
mcLM2_S_Ab_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet    0.92 0.218 21.0    0.465     1.37      1 a    
##  Fin juillet   1.33 0.394 19.8    0.506     2.15      1 a    
##  Juin          1.62 0.293 20.9    1.014     2.23      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_S_Ab_P_Tt <- Inv %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM2_S_Ab_P_Tt <- lmer(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM2_S_Ab_P_Tt)
eLM2_S_Ab_P_Tt<-emmeans(LM2_S_Ab_P_Tt,"Periode")
mcLM2_S_Ab_P_Tt<-cld(eLM2_S_Ab_P_Tt,ajust="tukey")
mcLM2_S_Ab_P_Tt$.group<-as.numeric(mcLM2_S_Ab_P_Tt$.group)
mcLM2_S_Ab_P_Tt$group[mcLM2_S_Ab_P_Tt$.group == 1] <- "a"
mcLM2_S_Ab_P_Tt$group[mcLM2_S_Ab_P_Tt$.group == 2] <- "b"
mcLM2_S_Ab_P_Tt$group[mcLM2_S_Ab_P_Tt$.group == 12] <- "ab"
mcLM2_S_Ab_P_Tt
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   1.85 0.251 43.2     1.34     2.35      1 a    
##  Mi-juillet    2.70 0.319 44.0     2.05     3.34     12 ab   
##  Juin          3.03 0.282 43.4     2.47     3.60      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM2_Ab_Po_G5<-emmeans(LM2_Ab_Po,"Gestion_moment_5")
mcLM2_Ab_Po_G5<-cld(eLM2_Ab_Po_G5,ajust="tukey")
mcLM2_Ab_Po_G5$.group<-as.numeric(mcLM2_Ab_Po_G5$.group)
mcLM2_Ab_Po_G5$group[mcLM2_Ab_Po_G5$.group == 1] <- "a"
mcLM2_Ab_Po_G5$group[mcLM2_Ab_Po_G5$.group == 2] <- "b"
mcLM2_Ab_Po_G5$group[mcLM2_Ab_Po_G5$.group == 3] <- "c"
mcLM2_Ab_Po_G5$group[mcLM2_Ab_Po_G5$.group == 12] <- "ab"
mcLM2_Ab_Po_G5$group[mcLM2_Ab_Po_G5$.group == 4] <- "d"
mcLM2_Ab_Po_G5
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte récente      1.23 0.278 96.5    0.679     1.78      1 a    
##  Graminées          2.36 0.479 42.4    1.393     3.32     12 ab   
##  Tonte tardive      2.53 0.214 58.4    2.100     2.96      2 b    
##  Fleuri             3.97 0.293 43.1    3.383     4.56      3 c    
##  Semé               5.73 0.384 38.5    4.952     6.50      4 d    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Po_G5_Ju <- Inv %>% 
  filter(Periode == "Juin")
LM2_Ab_Po_G5_Ju <- lm(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_Ab_Po_G5_Ju)
eLM2_Ab_Po_G5_Ju<-emmeans(LM2_Ab_Po_G5_Ju,"Gestion_moment_5")
mcLM2_Ab_Po_G5_Ju<-cld(eLM2_Ab_Po_G5_Ju,ajust="tukey")
mcLM2_Ab_Po_G5_Ju$.group<-as.numeric(mcLM2_Ab_Po_G5_Ju$.group)
mcLM2_Ab_Po_G5_Ju$group[mcLM2_Ab_Po_G5_Ju$.group == 1] <- "a"
mcLM2_Ab_Po_G5_Ju$group[mcLM2_Ab_Po_G5_Ju$.group == 2] <- "b"
mcLM2_Ab_Po_G5_Ju$group[mcLM2_Ab_Po_G5_Ju$.group == 12] <- "ab"
mcLM2_Ab_Po_G5_Ju$group[mcLM2_Ab_Po_G5_Ju$.group == 3] <- "c"
mcLM2_Ab_Po_G5_Ju
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Graminées          1.29 0.557 39    0.168     2.42     12 ab   
##  Tonte récente      1.44 0.408 39    0.616     2.26      1 a    
##  Tonte tardive      2.92 0.287 39    2.343     3.50      2 b    
##  Fleuri             4.50 0.341 39    3.806     5.19      3 c    
##  Semé               5.39 0.461 39    4.459     6.32      3 c    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Po_G5_mJ <- Inv %>% 
  filter(Periode == "Mi-juillet")
LM2_Ab_Po_G5_mJ <- lm(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_Ab_Po_G5_mJ)
eLM2_Ab_Po_G5_mJ<-emmeans(LM2_Ab_Po_G5_mJ,"Gestion_moment_5")
mcLM2_Ab_Po_G5_mJ<-cld(eLM2_Ab_Po_G5_mJ,ajust="tukey")
mcLM2_Ab_Po_G5_mJ$.group<-as.numeric(mcLM2_Ab_Po_G5_mJ$.group)
mcLM2_Ab_Po_G5_mJ$group[mcLM2_Ab_Po_G5_mJ$.group == 1] <- "a"
mcLM2_Ab_Po_G5_mJ$group[mcLM2_Ab_Po_G5_mJ$.group == 12] <- "ab"
mcLM2_Ab_Po_G5_mJ$group[mcLM2_Ab_Po_G5_mJ$.group == 23] <- "bc"
mcLM2_Ab_Po_G5_mJ$group[mcLM2_Ab_Po_G5_mJ$.group == 3] <- "c"
mcLM2_Ab_Po_G5_mJ$group[mcLM2_Ab_Po_G5_mJ$.group == 4] <- "d"
mcLM2_Ab_Po_G5_mJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente     0.932 0.341 39    0.241     1.62      1 a    
##  Graminées         1.627 0.631 39    0.351     2.90     12 ab   
##  Tonte tardive     2.929 0.365 39    2.190     3.67     23 bc   
##  Fleuri            4.168 0.381 39    3.399     4.94      3 c    
##  Semé              6.872 0.509 39    5.843     7.90      4 d    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM2_Ab_Po_G5_fJ <- Inv %>% 
  filter(Periode == "Fin juillet")
LM2_Ab_Po_G5_fJ <- lm(sqrt(Ab_Poll) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM2_Ab_Po_G5_fJ)
eLM2_Ab_Po_G5_fJ<-emmeans(LM2_Ab_Po_G5_fJ,"Gestion_moment_5")
mcLM2_Ab_Po_G5_fJ<-cld(eLM2_Ab_Po_G5_fJ,ajust="tukey")
mcLM2_Ab_Po_G5_fJ$.group<-as.numeric(mcLM2_Ab_Po_G5_fJ$.group)
mcLM2_Ab_Po_G5_fJ$group[mcLM2_Ab_Po_G5_fJ$.group == 1] <- "a"
mcLM2_Ab_Po_G5_fJ$group[mcLM2_Ab_Po_G5_fJ$.group == 2] <- "b"
mcLM2_Ab_Po_G5_fJ$group[mcLM2_Ab_Po_G5_fJ$.group == 12] <- "ab"
mcLM2_Ab_Po_G5_fJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente      1.26 0.585 35   0.0763     2.45      1 a    
##  Tonte tardive      1.86 0.289 35   1.2705     2.44      1 a    
##  Fleuri             3.18 0.459 35   2.2468     4.11     12 ab   
##  Graminées          4.18 0.739 35   2.6801     5.68      2 b    
##  Semé               5.03 0.542 35   3.9281     6.13      2 b    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.3 LMM 3. - N_interactions & Probabilité de visite selon la quantité de fleurs

proba d’être visité: +/- fct de la densité: N_int ~ qtté_plantes (par sp. de fleurs)

LM3_qtte <- lmer(N_Interactions ~ Qtte_Plantes + Gestion_moment_5 + Qtte_Plantes:Gestion_moment_5 + Area_gis_m_sq + Temperature + Periode + Periode:Gestion_moment_5 + (1|Parcelle), data = Interactions_Gestion)
# check_model(LM3_qtte)
# shapiro.test(residuals(LM3_qtte))

LM3_qtte <- lmer(sqrt(N_Interactions) ~ Qtte_Plantes + Gestion_moment_5 + Qtte_Plantes:Gestion_moment_5 + Area_gis_m_sq + Temperature + Periode + Periode:Gestion_moment_5 + (1|Parcelle), data = Interactions_Gestion)
# check_model(LM3_qtte)
# shapiro.test(residuals(LM3_qtte))

step(LM3_qtte, direction = "backward")
# sqrt(N_Interactions) ~ Qtte_Plantes + Gestion_moment_5 + Temperature + Periode + (1 | Parcelle) + Qtte_Plantes:Gestion_moment_5 + Gestion_moment_5:Periode
check_model(LM3_qtte)

Anova(LM3_qtte)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(N_Interactions)
##                                  Chisq Df Pr(>Chisq)    
## Qtte_Plantes                  270.3715  1  < 2.2e-16 ***
## Gestion_moment_5                4.9530  4   0.292151    
## Area_gis_m_sq                   2.0722  1   0.150008    
## Temperature                    31.3862  1  2.115e-08 ***
## Periode                        29.6885  2  3.575e-07 ***
## Qtte_Plantes:Gestion_moment_5  55.1174  4  3.070e-11 ***
## Gestion_moment_5:Periode       20.2190  8   0.009538 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(LM3_qtte)
#LM3_qtte

eLM3_qtte_P<-emmeans(LM3_qtte,"Periode")
mcLM3_qtte_P<-cld(eLM3_qtte_P,ajust="tukey")
mcLM3_qtte_P$.group<-as.numeric(mcLM3_qtte_P$.group)
mcLM3_qtte_P$group[mcLM3_qtte_P$.group == 1] <- "a"
mcLM3_qtte_P$group[mcLM3_qtte_P$.group == 2] <- "b"
mcLM3_qtte_P
##  Periode     emmean     SE  df lower.CL upper.CL .group group
##  Juin          1.20 0.0776 165     1.05     1.35      1 a    
##  Fin juillet   1.47 0.1001 118     1.27     1.66      2 b    
##  Mi-juillet    1.52 0.0826 196     1.36     1.69      2 b    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM3_qtte_G5<-emmeans(LM3_qtte,"Gestion_moment_5")
mcLM3_qtte_G5<-cld(eLM3_qtte_G5,ajust="tukey")
mcLM3_qtte_G5$.group<-as.numeric(mcLM3_qtte_G5$.group)
mcLM3_qtte_G5$group[mcLM3_qtte_G5$.group == 1] <- "a"
mcLM3_qtte_G5$group[mcLM3_qtte_G5$.group == 2] <- "b"
mcLM3_qtte_G5$group[mcLM3_qtte_G5$.group == 3] <- "c"
mcLM3_qtte_G5$group[mcLM3_qtte_G5$.group == 12] <- "ab"
mcLM3_qtte_G5$group[mcLM3_qtte_G5$.group == 4] <- "d"
mcLM3_qtte_G5
##  Gestion_moment_5 emmean     SE    df lower.CL upper.CL .group group
##  Fleuri             1.16 0.0804  33.4    0.995     1.32      1 a    
##  Tonte tardive      1.26 0.0649  58.4    1.129     1.39      1 a    
##  Tonte récente      1.36 0.1180 397.1    1.130     1.59      1 a    
##  Semé               1.37 0.1014  25.7    1.159     1.58      1 a    
##  Graminées          1.83 0.3260 136.0    1.189     2.48      1 a    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_P_G <- Interactions_Gestion %>% 
  filter(Gestion_moment_5 == "Graminées")
LM3_P_G <- lmer(sqrt(N_Interactions) ~ Qtte_Plantes + Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM3_P_G)
eLM3_P_G<-emmeans(LM3_P_G,"Periode")
mcLM3_P_G<-cld(eLM3_P_G,ajust="tukey")
mcLM3_P_G$.group<-as.numeric(mcLM3_P_G$.group)
mcLM3_P_G$group[mcLM3_P_G$.group == 1] <- "a"
mcLM3_P_G$group[mcLM3_P_G$.group == 2] <- "b"
mcLM3_P_G$group[mcLM3_P_G$.group == 12] <- "ab"
mcLM3_P_G
##  Periode     emmean    SE    df lower.CL upper.CL .group group
##  Juin          0.81 0.178 18.01    0.435     1.18      1 a    
##  Fin juillet   1.23 0.135  8.42    0.919     1.53     12 ab   
##  Mi-juillet    1.25 0.164 15.85    0.908     1.60      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_P_F <- Interactions_Gestion %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM3_P_F <- lmer(sqrt(N_Interactions) ~ Qtte_Plantes + Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM3_P_F)
eLM3_P_F<-emmeans(LM3_P_F,"Periode")
mcLM3_P_F<-cld(eLM3_P_F,ajust="tukey")
mcLM3_P_F$.group<-as.numeric(mcLM3_P_F$.group)
mcLM3_P_F$group[mcLM3_P_F$.group == 1] <- "a"
mcLM3_P_F$group[mcLM3_P_F$.group == 2] <- "b"
mcLM3_P_F$group[mcLM3_P_F$.group == 12] <- "ab"
mcLM3_P_F
##  Periode     emmean     SE   df lower.CL upper.CL .group group
##  Juin          1.03 0.0797 10.7    0.858     1.21      1 a    
##  Fin juillet   1.09 0.0954 20.9    0.890     1.29     12 ab   
##  Mi-juillet    1.22 0.0818 11.7    1.040     1.40      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_P_S <- Interactions_Gestion %>% 
  filter(Gestion_moment_5 == "Semé")
LM3_P_S <- lmer(sqrt(N_Interactions) ~ Qtte_Plantes +  Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM3_P_S)
eLM3_P_S<-emmeans(LM3_P_S,"Periode")
mcLM3_P_S<-cld(eLM3_P_S,ajust="tukey")
mcLM3_P_S$.group<-as.numeric(mcLM3_P_S$.group)
mcLM3_P_S$group[mcLM3_P_S$.group == 1] <- "a"
mcLM3_P_S
##  Periode     emmean     SE    df lower.CL upper.CL .group group
##  Juin          1.22 0.0536 11.89     1.10     1.34      1 a    
##  Fin juillet   1.24 0.0594 16.66     1.11     1.36      1 a    
##  Mi-juillet    1.31 0.0479  7.38     1.20     1.43      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_P_Tr <- Interactions_Gestion %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM3_P_Tr <- lmer(sqrt(N_Interactions) ~  Qtte_Plantes + Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM3_P_Tr)
eLM3_P_Tr<-emmeans(LM3_P_Tr,"Periode")
mcLM3_P_Tr<-cld(eLM3_P_Tr,ajust="tukey")
mcLM3_P_Tr$.group<-as.numeric(mcLM3_P_Tr$.group)
mcLM3_P_Tr$group[mcLM3_P_Tr$.group == 1] <- "a"
mcLM3_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin         0.756 0.216 19.7    0.304     1.21      1 a    
##  Mi-juillet   1.076 0.200 28.3    0.666     1.49      1 a    
##  Fin juillet  1.307 0.284 38.3    0.733     1.88      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_P_Tt <- Interactions_Gestion %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM3_P_Tt <- lmer(sqrt(N_Interactions) ~  Qtte_Plantes + Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM3_P_Tt)
eLM3_P_Tt<-emmeans(LM3_P_Tt,"Periode")
mcLM3_P_Tt<-cld(eLM3_P_Tt,ajust="tukey")
mcLM3_P_Tt$.group<-as.numeric(mcLM3_P_Tt$.group)
mcLM3_P_Tt$group[mcLM3_P_Tt$.group == 1] <- "a"
mcLM3_P_Tt$group[mcLM3_P_Tt$.group == 2] <- "b"
mcLM3_P_Tt$group[mcLM3_P_Tt$.group == 12] <- "ab"
mcLM3_P_Tt
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin          1.26 0.112 40.2     1.03     1.49      1 a    
##  Fin juillet   1.50 0.116 68.1     1.27     1.74     12 ab   
##  Mi-juillet    1.75 0.123 50.9     1.51     2.00      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_G5_Ju <- Interactions_Gestion %>% 
  filter(Periode == "Juin")
LM3_G5_Ju <- lmer(sqrt(N_Interactions) ~  Qtte_Plantes + Area_gis_m_sq + Temperature + Gestion_moment_5 + (1|Parcelle), data = CM3_G5_Ju)
eLM3_G5_Ju<-emmeans(LM3_G5_Ju,"Gestion_moment_5")
mcLM3_G5_Ju<-cld(eLM3_G5_Ju,ajust="tukey")
mcLM3_G5_Ju$.group<-as.numeric(mcLM3_G5_Ju$.group)
mcLM3_G5_Ju$group[mcLM3_G5_Ju$.group == 1] <- "a"
mcLM3_G5_Ju$group[mcLM3_G5_Ju$.group == 2] <- "b"
mcLM3_G5_Ju
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Graminées         0.909 0.274 38.0    0.353     1.46      1 a    
##  Tonte récente     1.082 0.179 77.9    0.725     1.44      1 a    
##  Fleuri            1.087 0.106 25.8    0.869     1.31      1 a    
##  Tonte tardive     1.216 0.108 40.5    0.998     1.43      1 a    
##  Semé              1.248 0.140 23.0    0.959     1.54      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_G5_mJ <- Interactions_Gestion %>% 
  filter(Periode == "Mi-juillet")
LM3_G5_mJ <-  lmer(sqrt(N_Interactions) ~  Qtte_Plantes + Area_gis_m_sq + Temperature + Gestion_moment_5 + (1|Parcelle), data = CM3_G5_mJ)
eLM3_G5_mJ<-emmeans(LM3_G5_mJ,"Gestion_moment_5")
mcLM3_G5_mJ<-cld(eLM3_G5_mJ,ajust="tukey")
mcLM3_G5_mJ$.group<-as.numeric(mcLM3_G5_mJ$.group)
mcLM3_G5_mJ$group[mcLM3_G5_mJ$.group == 1] <- "a"
mcLM3_G5_mJ$group[mcLM3_G5_mJ$.group == 2] <- "b"
mcLM3_G5_mJ$group[mcLM3_G5_mJ$.group == 3] <- "c"
mcLM3_G5_mJ
##  Gestion_moment_5 emmean    SE    df lower.CL upper.CL .group group
##  Tonte récente      1.02 0.163 166.2    0.701     1.34      1 a    
##  Fleuri             1.30 0.100  22.6    1.097     1.51      1 a    
##  Graminées          1.31 0.241  31.2    0.815     1.80      1 a    
##  Tonte tardive      1.36 0.113  39.4    1.131     1.59      1 a    
##  Semé               1.42 0.118  16.6    1.172     1.67      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM3_G5_fJ <- Interactions_Gestion %>% 
  filter(Periode == "Fin juillet")
LM3_G5_fJ <-  lmer(sqrt(N_Interactions) ~  Qtte_Plantes + Area_gis_m_sq + Temperature + Gestion_moment_5 + (1|Parcelle), data = CM3_G5_fJ)
eLM3_G5_fJ<-emmeans(LM3_G5_fJ,"Gestion_moment_5")
mcLM3_G5_fJ<-cld(eLM3_G5_fJ,ajust="tukey")
mcLM3_G5_fJ$.group<-as.numeric(mcLM3_G5_fJ$.group)
mcLM3_G5_fJ$group[mcLM3_G5_fJ$.group == 1] <- "a"
mcLM3_G5_fJ$group[mcLM3_G5_fJ$.group == 2] <- "b"
mcLM3_G5_fJ$group[mcLM3_G5_fJ$.group == 12] <- "ab"
mcLM3_G5_fJ
##  Gestion_moment_5 emmean     SE   df lower.CL upper.CL .group group
##  Tonte tardive     0.928 0.0893 47.8    0.748     1.11      1 a    
##  Tonte récente     1.097 0.2219 74.5    0.655     1.54      1 a    
##  Fleuri            1.219 0.1183 22.8    0.975     1.46      1 a    
##  Semé              1.388 0.1244 15.7    1.124     1.65      1 a    
##  Graminées         1.493 0.1883 21.5    1.102     1.88      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.4 LMM 4. - Sum(N_interactions)

Int_glm <- Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5, Area_gis_m_sq, Temperature, Parcelle) %>% 
  summarize(n= sum(N_Interactions))

LM4_Int <- lmer(n ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Int_glm)
# check_model(LM4_Int)
# shapiro.test(residuals(LM4_Int))

LM4_Int <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + (1|Parcelle), data = Int_glm)
# check_model(LM4_Int)
# shapiro.test(residuals(LM4_Int))

step(LM4_Int, direction = "backward")
# sqrt(n) ~ Temperature + Periode + Gestion_moment_5 + (1 | Parcelle) + Periode:Gestion_moment_5
check_model(LM4_Int)

Anova(LM4_Int)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(n)
##                            Chisq Df Pr(>Chisq)    
## Area_gis_m_sq             2.1736  1   0.140394    
## Temperature               5.0618  1   0.024459 *  
## Periode                   9.7773  2   0.007532 ** 
## Gestion_moment_5         73.7697  4  3.627e-15 ***
## Periode:Gestion_moment_5 16.3526  8   0.037603 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(LM4_Int)
#LM4_Int

eLM4_Int_P<-emmeans(LM4_Int,"Periode")
mcLM4_Int_P<-cld(eLM4_Int_P,ajust="tukey")
mcLM4_Int_P$.group<-as.numeric(mcLM4_Int_P$.group)
mcLM4_Int_P$group[mcLM4_Int_P$.group == 1] <- "a"
mcLM4_Int_P$group[mcLM4_Int_P$.group == 2] <- "b"
mcLM4_Int_P
##  Periode     emmean    SE  df lower.CL upper.CL .group group
##  Fin juillet   4.53 0.510 112     3.52     5.54      1 a    
##  Juin          4.73 0.455 105     3.83     5.63      1 a    
##  Mi-juillet    5.52 0.448 104     4.63     6.41      1 a    
## 
## Results are averaged over the levels of: Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_P_G <- Int_glm %>% 
  filter(Gestion_moment_5 == "Graminées")
LM4_P_G <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM4_P_G)
eLM4_P_G<-emmeans(LM4_P_G,"Periode")
mcLM4_P_G<-cld(eLM4_P_G,ajust="tukey")
mcLM4_P_G$.group<-as.numeric(mcLM4_P_G$.group)
mcLM4_P_G$group[mcLM4_P_G$.group == 1] <- "a"
mcLM4_P_G$group[mcLM4_P_G$.group == 2] <- "b"
mcLM4_P_G
##  Periode     emmean   SE   df lower.CL upper.CL .group group
##  Juin          1.06 1.58 5.85   -2.835     4.95      1 a    
##  Mi-juillet    2.71 1.53 5.87   -1.067     6.48      1 a    
##  Fin juillet   4.91 1.76 5.99    0.589     9.22      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_P_F <- Int_glm %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM4_P_F <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM4_P_F)
eLM4_P_F<-emmeans(LM4_P_F,"Periode")
mcLM4_P_F<-cld(eLM4_P_F,ajust="tukey")
mcLM4_P_F$.group<-as.numeric(mcLM4_P_F$.group)
mcLM4_P_F$group[mcLM4_P_F$.group == 1] <- "a"
mcLM4_P_F$group[mcLM4_P_F$.group == 2] <- "b"
mcLM4_P_F$group[mcLM4_P_F$.group == 12] <- "ab"
mcLM4_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   4.05 0.889 19.8     2.19     5.90      1 a    
##  Juin          5.91 0.799 16.3     4.21     7.60     12 ab   
##  Mi-juillet    6.30 0.796 16.2     4.62     7.99      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_P_S <- Int_glm %>% 
  filter(Gestion_moment_5 == "Semé")
LM4_P_S <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM4_P_S)
eLM4_P_S<-emmeans(LM4_P_S,"Periode")
mcLM4_P_S<-cld(eLM4_P_S,ajust="tukey")
mcLM4_P_S$.group<-as.numeric(mcLM4_P_S$.group)
mcLM4_P_S$group[mcLM4_P_S$.group == 1] <- "a"
mcLM4_P_S$group[mcLM4_P_S$.group == 2] <- "b"
mcLM4_P_S$group[mcLM4_P_S$.group == 12] <- "ab"
mcLM4_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   7.39 0.977 8.37     5.16     9.63      1 a    
##  Juin          8.42 0.950 7.91     6.22    10.61      1 a    
##  Mi-juillet   11.30 0.954 7.98     9.10    13.50      2 b    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_P_Tr <- Int_glm %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM4_P_Tr <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM4_P_Tr)
eLM4_P_Tr<-emmeans(LM4_P_Tr,"Periode")
mcLM4_P_Tr<-cld(eLM4_P_Tr,ajust="tukey")
mcLM4_P_Tr$.group<-as.numeric(mcLM4_P_Tr$.group)
mcLM4_P_Tr$group[mcLM4_P_Tr$.group == 1] <- "a"
mcLM4_P_Tr$group[mcLM4_P_Tr$.group == 2] <- "b"
mcLM4_P_Tr
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet    1.26 0.517 21.0    0.179     2.33      1 a    
##  Juin          2.57 0.693 20.9    1.123     4.01      1 a    
##  Fin juillet   2.61 0.934 19.8    0.664     4.56      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_P_Tt <- Int_glm %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM4_P_Tt <- lmer(sqrt(n) ~ Area_gis_m_sq + Temperature + Periode + (1|Parcelle), data = CM4_P_Tt)
eLM4_P_Tt<-emmeans(LM4_P_Tt,"Periode")
mcLM4_P_Tt<-cld(eLM4_P_Tt,ajust="tukey")
mcLM4_P_Tt$.group<-as.numeric(mcLM4_P_Tt$.group)
mcLM4_P_Tt$group[mcLM4_P_Tt$.group == 1] <- "a"
mcLM4_P_Tt$group[mcLM4_P_Tt$.group == 2] <- "b"
mcLM4_P_Tt$group[mcLM4_P_Tt$.group == 12] <- "ab"
mcLM4_P_Tt
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Fin juillet   3.44 0.700 43.4     2.03     4.85      1 a    
##  Juin          5.72 0.788 43.4     4.13     7.31      1 a    
##  Mi-juillet    6.03 0.892 44.0     4.23     7.83      1 a    
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM4_Int_G5<-emmeans(LM4_Int,"Gestion_moment_5")
mcLM4_Int_G5<-cld(eLM4_Int_G5,ajust="tukey")
mcLM4_Int_G5$.group<-as.numeric(mcLM4_Int_G5$.group)
mcLM4_Int_G5$group[mcLM4_Int_G5$.group == 1] <- "a"
mcLM4_Int_G5$group[mcLM4_Int_G5$.group == 2] <- "b"
mcLM4_Int_G5$group[mcLM4_Int_G5$.group == 3] <- "c"
mcLM4_Int_G5$group[mcLM4_Int_G5$.group == 12] <- "ab"
mcLM4_Int_G5$group[mcLM4_Int_G5$.group == 4] <- "d"
mcLM4_Int_G5
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte récente      1.89 0.606 99.0    0.683     3.09      1 a    
##  Graminées          3.02 1.002 42.5    1.003     5.05     12 ab   
##  Tonte tardive      5.02 0.456 60.9    4.112     5.93      2 b    
##  Fleuri             5.40 0.614 43.4    4.163     6.64      2 b    
##  Semé               9.29 0.799 38.1    7.672    10.91      3 c    
## 
## Results are averaged over the levels of: Periode 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_G5_Ju <- Int_glm %>% 
  filter(Periode == "Juin")
LM4_G5_Ju <- lm(sqrt(n) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM4_G5_Ju)
eLM4_G5_Ju<-emmeans(LM4_G5_Ju,"Gestion_moment_5")
mcLM4_G5_Ju<-cld(eLM4_G5_Ju,ajust="tukey")
mcLM4_G5_Ju$.group<-as.numeric(mcLM4_G5_Ju$.group)
mcLM4_G5_Ju$group[mcLM4_G5_Ju$.group == 1] <- "a"
mcLM4_G5_Ju$group[mcLM4_G5_Ju$.group == 2] <- "b"
mcLM4_G5_Ju
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Graminées          1.28 1.193 39   -1.133     3.69      1 a    
##  Tonte récente      1.96 0.873 39    0.189     3.72      1 a    
##  Tonte tardive      5.64 0.615 39    4.398     6.89      2 b    
##  Fleuri             5.73 0.730 39    4.252     7.21      2 b    
##  Semé               8.26 0.987 39    6.260    10.25      2 b    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_G5_mJ <- Int_glm %>% 
  filter(Periode == "Mi-juillet")
LM4_G5_mJ <- lm(sqrt(n) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM4_G5_mJ)
eLM4_G5_mJ<-emmeans(LM4_G5_mJ,"Gestion_moment_5")
mcLM4_G5_mJ<-cld(eLM4_G5_mJ,ajust="tukey")
mcLM4_G5_mJ$.group<-as.numeric(mcLM4_G5_mJ$.group)
mcLM4_G5_mJ$group[mcLM4_G5_mJ$.group == 1] <- "a"
mcLM4_G5_mJ$group[mcLM4_G5_mJ$.group == 2] <- "b"
mcLM4_G5_mJ$group[mcLM4_G5_mJ$.group == 3] <- "c"
mcLM4_G5_mJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente      1.28 0.731 39   -0.196     2.76      1 a    
##  Graminées          1.71 1.352 39   -1.026     4.44      1 a    
##  Fleuri             6.46 0.815 39    4.812     8.11      2 b    
##  Tonte tardive      6.55 0.783 39    4.966     8.13      2 b    
##  Semé              11.29 1.090 39    9.088    13.50      3 c    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM4_G5_fJ <- Int_glm %>% 
  filter(Periode == "Fin juillet")
LM4_G5_fJ <- lm(sqrt(n) ~ Area_gis_m_sq + Temperature + Gestion_moment_5, data = CM4_G5_fJ)
eLM4_G5_fJ<-emmeans(LM4_G5_fJ,"Gestion_moment_5")
mcLM4_G5_fJ<-cld(eLM4_G5_fJ,ajust="tukey")
mcLM4_G5_fJ$.group<-as.numeric(mcLM4_G5_fJ$.group)
mcLM4_G5_fJ$group[mcLM4_G5_fJ$.group == 1] <- "a"
mcLM4_G5_fJ$group[mcLM4_G5_fJ$.group == 2] <- "b"
mcLM4_G5_fJ$group[mcLM4_G5_fJ$.group == 12] <- "ab"
mcLM4_G5_fJ
##  Gestion_moment_5 emmean    SE df lower.CL upper.CL .group group
##  Tonte récente      1.82 1.247 35    -0.71     4.35      1 a    
##  Tonte tardive      3.40 0.615 35     2.15     4.65      1 a    
##  Fleuri             4.08 0.977 35     2.10     6.07     12 ab   
##  Graminées          5.69 1.576 35     2.49     8.89     12 ab   
##  Semé               8.30 1.155 35     5.95    10.64      2 b    
## 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## Note: contrasts are still on the sqrt scale 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.5 LMM 5. Nombre de visite par entité florale par heure - nvis/fl.h

nvis./fl. h: Q où sp présente dans les 3 Q, sp phare, comparer période/gestion

Interactions_Gestion %>% 
  count(Sp_Pollinisateurs) %>% 
  arrange(desc(n))
##                                Sp_Pollinisateurs   n
## 1                                 Apis mellifera 419
## 2                                                318
## 3                             Lasioglossum spec. 149
## 4                               Bombus pascuorum 110
## 5                                  Hylaeus spec. 103
## 6                              Bombus lapidarius 101
## 7                                  Syrphus spec.  87
## 8                           Episyrphus balteatus  73
## 9                              Anthophila indet.  71
## 10                               Syritta pipiens  49
## 11                               Eristalis tenax  39
## 12                     Bombus vestalis/bohemicus  36
## 13                              Syrphidae indet.  35
## 14                           Sphaerophoria spec.  34
## 15                         Sphaerophoria scripta  31
## 16    Bombus terrestris/lucorum/magnus/cryptarum  25
## 17                            Heriades truncorum  25
## 18                        Platycheirus albimanus  23
## 19                               Maniola jurtina  22
## 20                            Halictus scabiosae  21
## 21                             Eupeodes corollae  19
## 22           Lasioglossum (Leuchalictus) - spec.  17
## 23                            Polyommatus icarus  17
## 24                              Andrena flavipes  16
## 25              Lasioglossum (Dialictus) - spec.  15
## 26                               Eristalis spec.  14
## 27                               Megachile spec.  14
## 28                             Eristalis nemorum  12
## 29                            Eristalis pertinax  11
## 30                               Syrphus ribesii  11
## 31                                 Andrena rosae   9
## 32                                 Andrena spec.   9
## 33                               Sphecodes spec.   8
## 34                               Cheilosia spec.   7
## 35                               Andrena fulvago   6
## 36                           Helophilus pendulus   6
## 37                         Osmia leaiana/niveata   6
## 38                                Aricia agestis   4
## 39                               Bombus pratorum   4
## 40                          Eristalis arbustorum   4
## 41                              Myathropa florea   4
## 42                                  Pieris spec.   4
## 43                                Bombus lucorum   3
## 44                               Ceratina cyanea   3
## 45                          Chelostoma rapunculi   3
## 46                             Dasypoda hirtipes   3
## 47                              Eupeodes luniger   3
## 48         Lasioglossum (Sphecodogastra) - spec.   3
## 49                          Melanostoma mellinum   3
## 50 Melanostoma mellinum - Platycheirus albimanus   3
## 51                               Andrena humilis   2
## 52                             Cerceris arenaria   2
## 53                            Cerceris rybyensis   2
## 54                                Cerceris spec.   2
## 55           Colletes daviesanus/fodiens/similis   2
## 56                             Euclidia glyphica   2
## 57                             Halictidae indet.   2
## 58                        Helophilus trivittatus   2
## 59                       Megachile centuncularis   2
## 60                           Panurgus calcaratus   2
## 61                              Pyronia tithonus   2
## 62                                     Aglais io   1
## 63                               Andrena dorsata   1
## 64                          Andrena minutula-gr.   1
## 65                               Andrena proxima   1
## 66                                  Bombus spec.   1
## 67                               Bombus vestalis   1
## 68                            Carcharodus alceae   1
## 69                              Cheilosia pagana   1
## 70                          Cheilosia variabilis   1
## 71                      Dasysyrphus albostriatus   1
## 72                  Eristalis arbustorum/abusiva   1
## 73                        Eupeodes latifasciatus   1
## 74                              Hylaeus communis   1
## 75                              Hylaeus gredleri   1
## 76                      Lasioglossum leucozonium   1
## 77                       Lasioglossum sexnotatum   1
## 78                               Lycaena phlaeas   1
## 79                      Macroglossum stellatarum   1
## 80                       Megachile willughbiella   1
## 81                             Melanostoma spec.   1
## 82                          Mesembrina meridiana   1
## 83                                Neoascia spec.   1
## 84                                Nowickia ferox   1
## 85                               Osmia spinulosa   1
## 86                           Paragus haemorrhous   1
## 87                                   Pieris napi   1
## 88                                  Pieris rapae   1
## 89                               Pipizella spec.   1
## 90                             Pipizella viduata   1
## 91                               Scaeva pyrastri   1
## 92                             Scaeva selenitica   1
## 93                                  Scaeva spec.   1
## 94                        Stelis punctulatissima   1
## 95                             Stomorhina lunata   1
Interactions_nvis_fl_h <- Interactions_Gestion %>% 
  filter(Sp_Pollinisateurs == "Apis mellifera"|
         Sp_Pollinisateurs == "Bombus pascuorum"|
         Sp_Pollinisateurs == "Bombus lapidarius"|
         Sp_Pollinisateurs == "Lasioglossum spec."|
         Sp_Pollinisateurs == "Hylaeus spec."|
         Sp_Pollinisateurs == "Episyrphus balteatus")

Q1 <- Interactions_nvis_fl_h %>% 
  filter(Nombre_quadrats == "1") %>% 
  mutate(nvis_fl_h = N_Interactions/(Qtte_Plantes/2))

Q2 <- Interactions_nvis_fl_h %>% 
  filter(Nombre_quadrats == "2") %>% 
  mutate(nvis_fl_h = N_Interactions/(Qtte_Plantes/4))

Q3 <- Interactions_nvis_fl_h %>% 
  filter(Nombre_quadrats == "3") %>%
  mutate(nvis_fl_h = N_Interactions/(Qtte_Plantes/6))

Interactions_nvis_fl_h <- full_join(Q1, Q2)
Interactions_nvis_fl_h <- full_join(Interactions_nvis_fl_h, Q3)
  

Interactions_nvis_fl_h %>% ggplot(aes (x = Sp_Pollinisateurs, y = nvis_fl_h, color = Gestion_moment_5)) + 
  geom_boxplot(alpha = 0.70) +
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  labs(#title = "", 
    x = "Sp_poll", y = "nvis_fl_h") +
   theme(legend.position = "bottom") +

Interactions_nvis_fl_h %>% ggplot(aes (x = Sp_Pollinisateurs, y = nvis_fl_h, color = Periode)) + 
  geom_boxplot(alpha = 0.70) +
     scale_color_manual(values = c(Juin = "#74a9cf",
                                 `Mi-juillet` = "#2b8cbe",
                                 `Fin juillet` = "#045a8d")) + 
  labs(#title = "", 
    x = "Sp_poll", y = "nvis_fl_h") +
   theme(legend.position = "bottom")

LM5_nvis <- lmer(nvis_fl_h ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + Sp_Pollinisateurs + (1|Parcelle), data = Interactions_nvis_fl_h)
# check_model(LM5_nvis)
# shapiro.test(residuals(LM5_nvis))

LM5_nvis <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode * Gestion_moment_5 + Sp_Pollinisateurs + (1|Parcelle), data = Interactions_nvis_fl_h)
# check_model(LM5_nvis)
# shapiro.test(residuals(LM5_nvis))

step(LM5_nvis, direction = "backward")
# log(nvis_fl_h) ~ log(nvis_fl_h) ~ Temperature + Periode + Gestion_moment_5 + Sp_Pollinisateurs + (1 | Parcelle) + Periode:Gestion_moment_5
check_model(LM5_nvis)

Anova(LM5_nvis)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: log(nvis_fl_h)
##                            Chisq Df Pr(>Chisq)    
## Area_gis_m_sq             0.5189  1   0.471318    
## Temperature              24.5067  1  7.405e-07 ***
## Periode                  16.7118  2   0.000235 ***
## Gestion_moment_5         56.4344  4  1.626e-11 ***
## Sp_Pollinisateurs        15.0319  5   0.010227 *  
## Periode:Gestion_moment_5 19.4051  8   0.012837 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(LM5_nvis)
#LM5_nvis

eLM5_nvis_P<-emmeans(LM5_nvis,"Periode")
mcLM5_nvis_P<-cld(eLM5_nvis_P,ajust="tukey")
mcLM5_nvis_P$.group<-as.numeric(mcLM5_nvis_P$.group)
mcLM5_nvis_P$group[mcLM5_nvis_P$.group == 1] <- "a"
mcLM5_nvis_P$group[mcLM5_nvis_P$.group == 2] <- "b"
mcLM5_nvis_P
##  Periode     emmean    SE  df lower.CL upper.CL .group group
##  Juin        -1.501 0.161 178    -1.82   -1.182      1 a    
##  Mi-juillet  -1.021 0.141 124    -1.30   -0.742      2 b    
##  Fin juillet -0.899 0.145 126    -1.19   -0.611      2 b    
## 
## Results are averaged over the levels of: Gestion_moment_5, Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_P_G <- Interactions_nvis_fl_h %>% 
  filter(Gestion_moment_5 == "Graminées")
LM5_P_G <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode +  Sp_Pollinisateurs + (1|Parcelle), data = CM5_P_G)
eLM5_P_G<-emmeans(LM5_P_G,"Periode")
mcLM5_P_G<-cld(eLM5_P_G,ajust="tukey")
mcLM5_P_G$.group<-as.numeric(mcLM5_P_G$.group)
mcLM5_P_G$group[mcLM5_P_G$.group == 1] <- "a"
mcLM5_P_G$group[mcLM5_P_G$.group == 2] <- "b"
mcLM5_P_G$group[mcLM5_P_G$.group == 12] <- "ab"
mcLM5_P_G
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin        -3.274 1.316 78.1    -5.89   -0.654     12 ab   
##  Mi-juillet  -2.069 0.583 36.6    -3.25   -0.888      1 a    
##  Fin juillet -0.028 0.635 27.9    -1.33    1.273      2 b    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_P_F <- Interactions_nvis_fl_h %>% 
  filter(Gestion_moment_5 == "Fleuri")
LM5_P_F <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode + Sp_Pollinisateurs + (1|Parcelle), data = CM5_P_F)
eLM5_P_F<-emmeans(LM5_P_F,"Periode")
mcLM5_P_F<-cld(eLM5_P_F,ajust="tukey")
mcLM5_P_F$.group<-as.numeric(mcLM5_P_F$.group)
mcLM5_P_F$group[mcLM5_P_F$.group == 1] <- "a"
mcLM5_P_F$group[mcLM5_P_F$.group == 2] <- "b"
mcLM5_P_F$group[mcLM5_P_F$.group == 12] <- "ab"
mcLM5_P_F
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin        -0.954 0.145 19.5    -1.26   -0.651      1 a    
##  Mi-juillet  -0.774 0.148 20.4    -1.08   -0.466      1 a    
##  Fin juillet -0.622 0.228 69.3    -1.08   -0.166      1 a    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_P_S <- Interactions_nvis_fl_h %>% 
  filter(Gestion_moment_5 == "Semé")
LM5_P_S <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode + Sp_Pollinisateurs + (1|Parcelle), data = CM5_P_S)
eLM5_P_S<-emmeans(LM5_P_S,"Periode")
mcLM5_P_S<-cld(eLM5_P_S,ajust="tukey")
mcLM5_P_S$.group<-as.numeric(mcLM5_P_S$.group)
mcLM5_P_S$group[mcLM5_P_S$.group == 1] <- "a"
mcLM5_P_S$group[mcLM5_P_S$.group == 2] <- "b"
mcLM5_P_S$group[mcLM5_P_S$.group == 12] <- "ab"
mcLM5_P_S
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Mi-juillet  -0.273 0.149 5.27   -0.651    0.106      1 a    
##  Juin        -0.154 0.159 6.66   -0.534    0.227      1 a    
##  Fin juillet -0.128 0.175 9.56   -0.521    0.265      1 a    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_P_Tr <- Interactions_nvis_fl_h %>% 
  filter(Gestion_moment_5 == "Tonte récente")
LM5_P_Tr <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode + Sp_Pollinisateurs + (1|Parcelle), data = CM5_P_Tr)
eLM5_P_Tr<-emmeans(LM5_P_Tr,"Periode")
mcLM5_P_Tr<-cld(eLM5_P_Tr,ajust="tukey")
mcLM5_P_Tr$.group<-as.numeric(mcLM5_P_Tr$.group)
mcLM5_P_Tr$group[mcLM5_P_Tr$.group == 1] <- "a"
mcLM5_P_Tr$group[mcLM5_P_Tr$.group == 2] <- "b"
mcLM5_P_Tr$group[mcLM5_P_Tr$.group == 12] <- "ab"
mcLM5_P_Tr
##  Periode     emmean    SE    df lower.CL upper.CL .group group
##  Juin         -2.42 0.308 10.90    -3.10   -1.745      1 a    
##  Fin juillet  -1.57 0.449  5.92    -2.67   -0.466      1 a    
##  Mi-juillet   -1.34 0.365 12.72    -2.13   -0.545      1 a    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_P_Tt <- Interactions_nvis_fl_h %>% 
  filter(Gestion_moment_5 == "Tonte tardive")
LM5_P_Tt <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Periode + Sp_Pollinisateurs + (1|Parcelle), data = CM5_P_Tt)
eLM5_P_Tt<-emmeans(LM5_P_Tt,"Periode")
mcLM5_P_Tt<-cld(eLM5_P_Tt,ajust="tukey")
mcLM5_P_Tt$.group<-as.numeric(mcLM5_P_Tt$.group)
mcLM5_P_Tt$group[mcLM5_P_Tt$.group == 1] <- "a"
mcLM5_P_Tt$group[mcLM5_P_Tt$.group == 2] <- "b"
mcLM5_P_Tt$group[mcLM5_P_Tt$.group == 12] <- "ab"
mcLM5_P_Tt
##  Periode     emmean    SE   df lower.CL upper.CL .group group
##  Juin         -2.24 0.171 49.7    -2.58   -1.894      1 a    
##  Mi-juillet   -1.47 0.178 57.6    -1.83   -1.116      2 b    
##  Fin juillet  -1.28 0.175 74.9    -1.62   -0.927      2 b    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM5_nvis_G5<-emmeans(LM5_nvis,"Gestion_moment_5")
mcLM5_nvis_G5<-cld(eLM5_nvis_G5,ajust="tukey")
mcLM5_nvis_G5$.group<-as.numeric(mcLM5_nvis_G5$.group)
mcLM5_nvis_G5$group[mcLM5_nvis_G5$.group == 1] <- "a"
mcLM5_nvis_G5$group[mcLM5_nvis_G5$.group == 12] <- "ab"
mcLM5_nvis_G5$group[mcLM5_nvis_G5$.group == 23] <- "bc"
mcLM5_nvis_G5$group[mcLM5_nvis_G5$.group == 3] <- "c"
mcLM5_nvis_G5
##  Gestion_moment_5 emmean    SE    df lower.CL upper.CL .group group
##  Tonte tardive    -1.803 0.126  56.6   -2.056   -1.549      1 a    
##  Graminées        -1.542 0.395  60.7   -2.331   -0.752     12 ab   
##  Tonte récente    -1.408 0.217 251.4   -1.835   -0.980     12 ab   
##  Fleuri           -0.751 0.161  36.1   -1.077   -0.426     23 bc   
##  Semé             -0.198 0.196  24.7   -0.601    0.206      3 c    
## 
## Results are averaged over the levels of: Periode, Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_G5_Ju <- Interactions_nvis_fl_h %>% 
  filter(Periode == "Juin")
LM5_G5_Ju <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Gestion_moment_5 + Sp_Pollinisateurs + (1|Parcelle), data = CM5_G5_Ju)
eLM5_G5_Ju<-emmeans(LM5_G5_Ju,"Gestion_moment_5")
mcLM5_G5_Ju<-cld(eLM5_G5_Ju,ajust="tukey")
mcLM5_G5_Ju$.group<-as.numeric(mcLM5_G5_Ju$.group)
mcLM5_G5_Ju$group[mcLM5_G5_Ju$.group == 1] <- "a"
mcLM5_G5_Ju$group[mcLM5_G5_Ju$.group == 2] <- "b"
mcLM5_G5_Ju$group[mcLM5_G5_Ju$.group == 12] <- "ab"
mcLM5_G5_Ju
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte récente     -2.19 0.458 76.2    -3.10  -1.2737      1 a    
##  Tonte tardive     -1.99 0.265 35.4    -2.52  -1.4496      1 a    
##  Graminées         -1.85 0.930 38.1    -3.74   0.0287     12 ab   
##  Fleuri            -1.08 0.260 27.4    -1.62  -0.5477     12 ab   
##  Semé              -0.37 0.333 22.4    -1.06   0.3202      2 b    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_G5_mJ <- Interactions_nvis_fl_h %>% 
  filter(Periode == "Mi-juillet")
LM5_G5_mJ <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Gestion_moment_5 + Sp_Pollinisateurs + (1|Parcelle), data = CM5_G5_mJ)
eLM5_G5_mJ<-emmeans(LM5_G5_mJ,"Gestion_moment_5")
mcLM5_G5_mJ<-cld(eLM5_G5_mJ,ajust="tukey")
mcLM5_G5_mJ$.group<-as.numeric(mcLM5_G5_mJ$.group)
mcLM5_G5_mJ$group[mcLM5_G5_mJ$.group == 1] <- "a"
mcLM5_G5_mJ$group[mcLM5_G5_mJ$.group == 2] <- "b"
mcLM5_G5_mJ$group[mcLM5_G5_mJ$.group == 12] <- "ab"
mcLM5_G5_mJ
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte tardive    -1.852 0.237 41.3   -2.331   -1.373      1 a    
##  Graminées        -1.374 0.570 41.5   -2.525   -0.223     12 ab   
##  Tonte récente    -1.027 0.374 77.9   -1.773   -0.282     12 ab   
##  Fleuri           -0.656 0.234 36.6   -1.129   -0.182      2 b    
##  Semé             -0.424 0.268 25.5   -0.976    0.127      2 b    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
CM5_G5_fJ <- Interactions_nvis_fl_h %>% 
  filter(Periode == "Fin juillet")
LM5_G5_fJ <- lmer(log(nvis_fl_h) ~ Area_gis_m_sq + Temperature + Gestion_moment_5 + Sp_Pollinisateurs + (1|Parcelle), data = CM5_G5_fJ)
eLM5_G5_fJ<-emmeans(LM5_G5_fJ,"Gestion_moment_5")
mcLM5_G5_fJ<-cld(eLM5_G5_fJ,ajust="tukey")
mcLM5_G5_fJ$.group<-as.numeric(mcLM5_G5_fJ$.group)
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 1] <- "a"
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 2] <- "b"
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 12] <- "ab"
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 23] <- "bc"
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 3] <- "c"
mcLM5_G5_fJ$group[mcLM5_G5_fJ$.group == 123] <- "abc"
mcLM5_G5_fJ
##  Gestion_moment_5 emmean    SE   df lower.CL upper.CL .group group
##  Tonte récente    -1.904 0.441 38.8   -2.797   -1.011     12 ab   
##  Tonte tardive    -1.834 0.229 44.6   -2.295   -1.374      1 a    
##  Graminées        -0.703 0.563 31.0   -1.852    0.446    123 abc  
##  Fleuri           -0.457 0.302 29.3   -1.075    0.161     23 bc   
##  Semé             -0.179 0.313 16.9   -0.841    0.482      3 c    
## 
## Results are averaged over the levels of: Sp_Pollinisateurs 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 5 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.
eLM5_nvis_spP<-emmeans(LM5_nvis,"Sp_Pollinisateurs")
mcLM5_nvis_spP<-cld(eLM5_nvis_spP,ajust="tukey")
mcLM5_nvis_spP$.group<-as.numeric(mcLM5_nvis_spP$.group)
mcLM5_nvis_spP$group[mcLM5_nvis_spP$.group == 1] <- "a"
mcLM5_nvis_spP$group[mcLM5_nvis_spP$.group == 2] <- "b"
mcLM5_nvis_spP$group[mcLM5_nvis_spP$.group == 12] <- "ab"
mcLM5_nvis_spP
##  Sp_Pollinisateurs    emmean    SE    df lower.CL upper.CL .group group
##  Apis mellifera        -1.38 0.120  74.3    -1.62   -1.142      1 a    
##  Bombus lapidarius     -1.21 0.150 165.0    -1.51   -0.918     12 ab   
##  Bombus pascuorum      -1.20 0.153 171.1    -1.50   -0.893     12 ab   
##  Hylaeus spec.         -1.08 0.167 221.2    -1.41   -0.751     12 ab   
##  Episyrphus balteatus  -1.02 0.176 273.0    -1.37   -0.674     12 ab   
##  Lasioglossum spec.    -0.95 0.137 128.3    -1.22   -0.679      2 b    
## 
## Results are averaged over the levels of: Periode, Gestion_moment_5 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 6 estimates 
## significance level used: alpha = 0.05 
## NOTE: Compact letter displays can be misleading
##       because they show NON-findings rather than findings.
##       Consider using 'pairs()', 'pwpp()', or 'pwpm()' instead.

3.2.6 LMM 6. Tonte

3.2.6.1 S

LM6_Tonte_S <- lmer(S ~ Jour_af_tonte + (1|Site), data = expe_Tonte)
# check_model(LM6_Tonte_S)
# shapiro.test(residuals(LM6_Tonte_S))

LM6_Tonte_S <- lmer((S)^2 ~ Jour_af_tonte + (1|Site), data = expe_Tonte)
# check_model(LM6_Tonte_S)
# shapiro.test(residuals(LM6_Tonte_S))

step(LM6_Tonte_S, direction = "backward")
# (S)^2 ~ Jour_af_tonte
check_model(LM6_Tonte_S)

Anova(LM6_Tonte_S)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: (S)^2
##                Chisq Df Pr(>Chisq)    
## Jour_af_tonte 23.293  1  1.391e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(LM6_Tonte_S)
#LM6_Tonte_S

3.2.6.2 Ab

LM6_Tonte_Ab <- lmer(Ab ~ Jour_af_tonte + (1|Site), data = expe_Tonte)
# check_model(LM6_Tonte_Ab)
# shapiro.test(residuals(LM6_Tonte_Ab))
LM6_Tonte_Ab <- lmer(sqrt(Ab) ~ Jour_af_tonte + (1|Site), data = expe_Tonte)

step(LM6_Tonte_Ab, direction = "backward")
# sqrt(Ab) ~ Jour_af_tonte + (1 | Site)
check_model(LM6_Tonte_Ab)

Anova(LM6_Tonte_Ab)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: sqrt(Ab)
##                Chisq Df Pr(>Chisq)    
## Jour_af_tonte 114.88  1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#summary(LM6_Tonte_Ab)
#LM6_Tonte_Ab

4 Autres analyses

4.1 Interactions vs pas d’interactions: tests d’homogénéité

Interactions_Gestion$Inter_YN <- ifelse(Interactions_Gestion$N_Interactions == 0, 0, 1)
Interactions_Gestion %>% 
  group_by(Gestion_2, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = Inter_YN, y = n, color = Gestion_2)) +
  geom_point(size = 3)

Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = Inter_YN, y = n, color = Gestion_moment_5)) +
  geom_jitter(size = 3, width = 0.15) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 

a <- Interactions_Gestion %>% 
  group_by(Gestion_moment_5) %>%  
  summarize(a = n())

b <- Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>% 
  summarize(n = n()) 
c <- full_join(a,b)

c %>% 
  mutate(prop = n/a) %>% 
  filter(Inter_YN == 1) %>% 
  ggplot(aes(x = Gestion_moment_5, y = prop, color = Gestion_moment_5)) +
  geom_point(size = 3) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 

Interactions_Gestion <- Interactions_Gestion %>% 
  mutate(Inter_YN = as.factor(Inter_YN))
levels(Interactions_Gestion$Inter_YN) <- c("Pas d'interactions","Interactions")

Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>% 
  summarize(n = n()) %>% 
  ggplot(aes(x = Gestion_moment_5, y = n, color = Inter_YN)) +
  geom_jitter(size = 3, width = 0.15) 

Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>%
  summarize(n = n()) %>% 
  ggplot(aes(x = Gestion_moment_5, y = n, color = Gestion_moment_5)) +
  facet_wrap(~Inter_YN) +
  geom_boxplot(alpha = 0.70) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) 

4.2 Espèces de plantes ayant zero interactions

Totalité des quadrats

Pl_zero <- Interactions_Gestion %>% 
  replace(is.na(.), 0) %>% 
  group_by(Sp_Plantes) %>% 
  summarise(data = sum(N_Interactions)) %>% 
  filter(data == 0)
Pl_zero 
## # A tibble: 17 x 2
##    Sp_Plantes                data
##    <chr>                    <int>
##  1 ""                           0
##  2 "Cerastium fontanum"         0
##  3 "Galium mollugo"             0
##  4 "Geranium dissectum"         0
##  5 "Geranium molle"             0
##  6 "Lapsana communis"           0
##  7 "Lathyrus latifolius"        0
##  8 "Plantago lanceolata"        0
##  9 "Ranunculus acris"           0
## 10 "Rhinanthus minor"           0
## 11 "Tanacetum vulgare"          0
## 12 "Trifolium campestre"        0
## 13 "Trifolium dubium"           0
## 14 "Veronica arvensis"          0
## 15 "Veronica persica"           0
## 16 "Veronica serpyllifolia"     0
## 17 "Vicia sativa"               0

16 espèces de plantes non-visitées, dans aucun quadrat!

4.3 Nombre de changements de fleurs/sp

pour sp de poll. x individus : +ieurs fleurs en séquences

Interactions_indiv <-  Interactions_Classes %>% 
  filter(!is.na(individu)) %>% 
  mutate(Sp_pollinisateurs = Sp_Pollinisateurs) %>% 
  unite('Sp_unique', Sp_Pollinisateurs:individu)

seq <- Interactions_indiv %>% 
  count(Sp_unique) %>% 
  arrange(desc(n))
indiv_seq <- full_join(Interactions_indiv,seq)
sum <- Interactions_indiv %>% 
  group_by(Sp_unique) %>% 
  summarize(sum = sum(N_Interactions)) %>% 
  arrange(desc(sum))
join_sum <- full_join(Interactions_indiv, sum)
join_sum_n <- full_join(join_sum, indiv_seq)


join_sum_n %>% 
  ggplot(aes(x= Classe_Poll, y= sum, color = Gestion_2))+
  geom_boxplot(alpha = 0.70)

join_sum_n %>% 
  ggplot(aes(x= Sp_pollinisateurs, y= sum, color = Gestion_2))+
  geom_point()

join_sum_n %>% 
  mutate(n = as.factor(n)) %>% 
  ggplot(aes(x= Sp_pollinisateurs, y= sum, color = n))+
  geom_point()

join_sum_n %>% 
  mutate(n = as.factor(n)) %>% 
  ggplot(aes(x= Classe_Poll, y= sum, color = n))+
  geom_boxplot(alpha = 0.70)

join_sum_n %>% ggplot(aes(x= Sp_pollinisateurs, y= n, color = Classe_Poll))+
  geom_point()

join_sum_n %>% ggplot(aes(x= Classe_Poll, y= n, color = Gestion_2))+
  geom_point(size = 4)

Interactions_Gestion %>% 
  select(Site_gestion_date_Quadrat, Gestion_moment_5, Sp_Plantes, Sp_Pollinisateurs, individu, N_Interactions) %>% 
  filter(individu == "1315"|
           individu == "1434"|
           individu == "1325"|
           individu == "749"|
           individu == "536"|
           individu == "1202"|
           individu == "755")
##       Site_gestion_date_Quadrat Gestion_moment_5           Sp_Plantes
## 1      CAV_Fauche_finJuillet_Q1             Semé      Centaurea jacea
## 2      CAV_Fauche_finJuillet_Q1             Semé   Trifolium pratense
## 3      CAV_Fauche_finJuillet_Q1             Semé     Knautia arvensis
## 4      CAV_Fauche_finJuillet_Q2             Semé Betonica officinalis
## 5      CAV_Fauche_finJuillet_Q2             Semé    Prunella vulgaris
## 6      CAV_Fauche_finJuillet_Q2             Semé      Centaurea jacea
## 7       CAV_Fauche_miJuillet_Q2             Semé      Centaurea jacea
## 8       CAV_Fauche_miJuillet_Q2             Semé Betonica officinalis
## 9       CAV_Fauche_miJuillet_Q2             Semé Betonica officinalis
## 10      CAV_Fauche_miJuillet_Q2             Semé    Prunella vulgaris
## 11      CAV_Fauche_miJuillet_Q2             Semé    Prunella vulgaris
## 12      CAV_Fauche_miJuillet_Q2             Semé   Trifolium pratense
## 13       Kellner_Fauche_Juin_Q3             Semé Achillea millefolium
## 14       Kellner_Fauche_Juin_Q3             Semé    Jacobaea vulgaris
## 15       Kellner_Fauche_Juin_Q3             Semé     Origanum vulgare
## 16  Kellner_Fauche_miJuillet_Q1             Semé Hypericum perforatum
## 17  Kellner_Fauche_miJuillet_Q1             Semé    Jacobaea vulgaris
## 18  Kellner_Fauche_miJuillet_Q1             Semé   Lotus corniculatus
## 19 Reaumur_Fauche_finJuillet_Q1             Semé    Cichorium intybus
## 20 Reaumur_Fauche_finJuillet_Q1             Semé      Cirsium arvense
## 21 Reaumur_Fauche_finJuillet_Q1             Semé      Centaurea jacea
##      Sp_Pollinisateurs individu N_Interactions
## 1     Bombus pascuorum     1315              1
## 2     Bombus pascuorum     1315              1
## 3     Bombus pascuorum     1315              2
## 4     Bombus pascuorum     1325              1
## 5     Bombus pascuorum     1325              4
## 6     Bombus pascuorum     1325              1
## 7     Bombus pascuorum      749              2
## 8     Bombus pascuorum      749              1
## 9     Bombus pascuorum      755              9
## 10    Bombus pascuorum      749              3
## 11    Bombus pascuorum      755              3
## 12    Bombus pascuorum      755              3
## 13 Helophilus pendulus      536              1
## 14 Helophilus pendulus      536              1
## 15 Helophilus pendulus      536              1
## 16     Megachile spec.     1202              1
## 17     Megachile spec.     1202              3
## 18     Megachile spec.     1202              1
## 19   Bombus lapidarius     1434              1
## 20   Bombus lapidarius     1434              2
## 21   Bombus lapidarius     1434              2
length(unique(Interactions_indiv$Sp_Plantes))
## [1] 33

5 Graphiques finaux

5.1 Heure

ggplot(Inv) +
  aes(x = Heure_debut, y = Ab_Poll, color = Gestion_moment_5) +
  geom_point()+ 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  scale_x_discrete(breaks = c("09:00:00","10:00:00","11:00:00","12:00:00","13:00:00","14:00:00","15:00:00","16:00:00", "17:00:00")) +
  labs(x = "Heure", 
       y = "Abondance en pollinisateurs", 
       color = "Type de gestion") +
  theme(legend.position="bottom",
        legend.title = element_text(size= 12),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14))

5.2 Meteo

ggplot(Inv) +
  aes(x = Gestion_moment_5, y = Ab_Poll, color = Meteo) +
  geom_boxplot(alpha = 0.70) +
  scale_color_manual(values = c("Nuageux" = "#7f908c",
                                "Alternances" = "#79ccbd",
                                "Soleil" = "#fbcb09")) +
  labs(x = "Type de gestion", 
       y = "Abondance en pollinisateurs", 
       color = "Météo") +
  theme(legend.position="bottom",
        legend.title = element_text(size= 12),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14))

5.3 Courbes d’accumulation

5.3.1 Plantes

ggplot(data=accum.long_Plantes_G2, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
    scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
    geom_line(aes(colour=Grouping), size=1.5) +
    geom_point(data=subset(accum.long_Plantes_G2, labelit==TRUE), 
               aes(colour=Grouping, shape=Grouping), size=2.5) +
      scale_shape_manual(values = c("Fauche" = 16,
                                  "Tonte" = 5)) +
    geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
                show.legend=FALSE) + 
    scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
    labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de plantes", colour = "Type de gestion", shape = "Type de gestion") + 
  theme(legend.position = c(0.85,0.07),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
ggplot(data=accum.long_Plantes_G5, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
  scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  geom_line(aes(colour=Grouping), size=1.5) +
  geom_point(data=subset(accum.long_Plantes_G5, labelit==T), 
             aes(colour=Grouping, shape=Grouping), size=2.5) +
  scale_shape_manual(values = c("Graminées" = 16,
                                "Fleuri" = 17,
                                "Semé" = 15,
                                "Tonte récente" = 5,
                                "Tonte tardive" = 6)) +
  geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
              show.legend=FALSE) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de plantes", colour = "Type de gestion", shape = "Type de gestion") +
  theme(legend.position = c(0.82,0.12),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14))  

5.3.2 Pollinisateurs

ggplot(data=accum.long_Poll_G2, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
  scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  geom_line(aes(colour=Grouping), size=1.5) +
  geom_point(data=subset(accum.long_Poll_G2, labelit==TRUE), 
             aes(colour=Grouping, shape=Grouping), size=2.5) +
  scale_shape_manual(values = c("Fauche" = 16,
                                "Tonte" = 5)) +
  geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
              show.legend=FALSE) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de pollinisateurs", colour = "Type de gestion", shape = "Type de gestion") + 
    theme(legend.position = c(0.85,0.07),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
ggplot(data=accum.long_Poll_G5, aes(x = Sites, y = Richness, ymax = UPR, ymin = LWR)) + 
  scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
  geom_line(aes(colour=Grouping), size=1.5) +
  geom_point(data=subset(accum.long_Poll_G5, labelit==T), 
             aes(colour=Grouping, shape=Grouping), size=2.5) +
  scale_shape_manual(values = c("Graminées" = 16,
                                "Fleuri" = 17,
                                "Semé" = 15,
                                "Tonte récente" = 5,
                                "Tonte tardive" = 6)) + 
  geom_ribbon(aes(colour=Grouping, fill=after_scale(alpha(colour, alpha=0.15))), 
              show.legend=FALSE) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Nombre d'échantillonages", y = "Nombre d'espèces de pollinisateurs", colour = "Type de gestion", shape = "Type de gestion") +
  theme(legend.position = c(0.82,0.12),
        legend.text = element_text(size=10),
        axis.text=element_text(size=12),
        axis.title=element_text(size=14))  

5.4 Analyses multivariées

Voir précédement

5.5 Modèles Inventaires

5.5.1 LM1_S_Pl

Inv %>% ggplot(aes (x = Gestion_2, y = S_Plant)) + 
  geom_boxplot(aes (color = Gestion_2), alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en plantes") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
  geom_text(aes(label = "b", y = 14, x = 1),color="black") +
  geom_text(aes(label = "a", y = 7, x = 2),color="black")

5.5.2 LM1_Ab_Pl

Inv %>% ggplot(aes (x = Periode, y = Ab_Plant)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) + 
  labs(#title = "Abondance en plantes en fonction des types de gestion", 
    x = "Période", y = "Abondance en plantes") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
  geom_text(aes(label = "b", y = 265, x =1),color="black") +
  geom_text(aes(label = "a", y = 210, x =2),color="black") +
  geom_text(aes(label = "a", y = 160, x =3),color="black") +
Inv %>% ggplot(aes (x = Temperature, y = Ab_Plant)) + 
  geom_point() +
  geom_smooth(method = "lm", se = T) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Température (°C)", y = "Abondance en plantes") + 
  theme(axis.text=element_text(size=12),
        axis.title=element_text(size=14))

5.5.3 LM1_S_Po

Inv %>% ggplot(aes (x = Gestion_2, y = S_Poll)) + 
  geom_boxplot(aes (color = Gestion_2), alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Richesse spécifique en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en pollinisateurs") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
  geom_text(aes(label = "b", y = 21.5, x =1),color="black") +
  geom_text(aes(label = "a", y = 10, x =2),color="black")

5.5.4 LM1_Ab_Po

Inv %>% ggplot(aes (x = Gestion_2, y = Ab_Poll)) + 
  geom_boxplot(aes (color = Gestion_2), alpha = 0.70) + 
  scale_color_manual(values = c("Fauche" = "#aa1e0f",
                                "Tonte" = "#12661f")) +
  labs(#title = "Abondance en pollinisateurs en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en pollinisateurs") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
         geom_text(aes(label = "b", y = 55, x =1),color="black") +
  geom_text(aes(label = "a", y = 19, x =2),color="black")

5.5.5 LM2_S_Pl

Inv %>% ggplot(aes (x = Periode, y = S_Plant)) +
  geom_boxplot(aes (color = Periode), alpha = 0.70) +
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de plantes en fonction des types de gestion",
    x = "Période", y = "Richesse spécifique en plantes", color = "Période") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
  geom_text(aes(label = "a", y = 11.5, x =1),color="black") +
  geom_text(aes(label = "a", y = 9.5, x =2),color="black") +
  geom_text(aes(label = "a", y = 7.5, x =3),color="black") +
Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Plant)) + 
   geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",#6dcf20
                                "Tonte tardive" = "#2b790c")) + #12661f
  labs(#title = "Richesse spécifique en plantes en fonction des types de gestion", 
    x = "Type de gestion", y = "Richesse spécifique en plantes") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
  geom_text(aes(label = "a", y = 6, x =1),color="black") +
  geom_text(aes(label = "a", y = 5, x =4),color="black") +
  geom_text(aes(label = "b", y = 7, x =5),color="black") +
  geom_text(aes(label = "bc", y = 12, x =2),color="black") +
  geom_text(aes(label = "c", y = 18, x =3),color="black") 

5.5.6 LM2_Ab_Pl

Inv %>% ggplot(aes (x = Periode, y = Ab_Plant)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) +
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "Période", y = "Abondance en plantes", color = "Période") +
  theme(legend.position = "none",
        axis.text=element_text(size=12),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "b", y = 265, x =1),color="black") +
  geom_text(aes(label = "a", y = 210, x =2),color="black") +
  geom_text(aes(label = "a", y = 160, x =3),color="black") +
  Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Plant)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(#title = "Abondance de plantes en fonction des types de gestion", 
    x = "Type de gestion", y = "Abondance en plantes", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
      geom_text(aes(label = "a", y = 60, x =1),color="black") +
  geom_text(aes(label = "b", y = 270, x =2),color="black") +
  geom_text(aes(label = "b", y = 270, x =3),color="black") +
      geom_text(aes(label = "a", y = 110, x =4),color="black") +
  geom_text(aes(label = "b", y = 210, x =5),color="black") 

5.5.7 LM2_S_Po

Inv %>% ggplot(aes (x = Periode, y = S_Poll)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) +
  labs(x = "Période", y = "Richesse spécifique en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "a", y = 16.5, x = 1)) +
  geom_text(aes(label = "a", y = 19.5, x = 2)) +
  geom_text(aes(label = "a", y = 16.5, x = 3)) +
Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Poll)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Richesse spécifique en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "ab", y = 12.5, x = 1)) +
  geom_text(aes(label = "c", y = 17.5, x = 2)) +
  geom_text(aes(label = "c", y = 21.5, x = 3)) +
  geom_text(aes(label = "a", y = 5.5, x = 4)) +
  geom_text(aes(label = "b", y = 9.5, x = 5))

data_text <- data.frame(label = c("a", "b", "b","a", "a", 
                                  "ab", "cd", "d","a", "bc",
                                  "b", "b", "b","a", "a"),
                        Periode = rep(c("Juin", "Mi-juillet", "Fin juillet"), each = 5),
                        x = c(1,2,3,4,5,
                              1,2,3,4,5,
                              1,2,3,4,5),
                        y = c(9,14,17,6,10,
                              10,18,20,5,6,
                              13,10,11,3,6))
data_text$Periode <- fct_relevel(data_text$Periode, c("Juin", "Mi-juillet", "Fin juillet"))
Inv %>% ggplot(aes (x = Gestion_moment_5, y = S_Poll)) + 
  facet_wrap(~Periode)+
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Richesse spécifique en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "bottom",
        legend.title = element_text(size=11), 
        legend.text = element_text(size=10),
        axis.text.y = element_text(size=10),
        axis.text.x = element_text(size=8),
        axis.title = element_text(size=14),
        strip.text = element_text(size=10)) +
   geom_text(data = data_text %>% filter(Periode == Periode), 
             mapping = aes(x = x, y = y, label = label),
             size = 4, fontface = "bold")

5.5.8 LM2_Ab_Po

Inv %>% ggplot(aes (x = Periode, y = Ab_Poll)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) +
  labs(x = "Période", y = "Abondance en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "a", y = 42.5, x = 1)) +
  geom_text(aes(label = "a", y = 45, x = 2)) +
  geom_text(aes(label = "a", y = 29, x = 3)) +
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Poll)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Abondance en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "ab", y = 17.5, x = 1)) +
  geom_text(aes(label = "c", y = 45, x = 2)) +
  geom_text(aes(label = "d", y = 62, x = 3)) +
  geom_text(aes(label = "a", y = 9, x = 4)) +
  geom_text(aes(label = "b", y = 19, x = 5))

data_text <- data.frame(label = c("ab", "c", "c","a", "b", 
                                  "ab", "c", "d","a", "bc",
                                  "b", "ab", "b","a", "a"),
                        Periode = rep(c("Juin", "Mi-juillet", "Fin juillet"), each = 5),
                        x = c(1,2,3,4,5,
                              1,2,3,4,5,
                              1,2,3,4,5),
                        y = c(16,40,43,9,29,
                              19,45,63,6,20,
                              45,25,40,6,19))
data_text$Periode <- fct_relevel(data_text$Periode, c("Juin", "Mi-juillet", "Fin juillet"))
Inv %>% ggplot(aes (x = Gestion_moment_5, y = Ab_Poll)) + 
  facet_wrap(~Periode)+
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Abondance en pollinisateurs", color = "Type de gestion") +
  theme(legend.position = "bottom",
        legend.title = element_text(size=11), 
        legend.text = element_text(size=10),
        axis.text.y = element_text(size=10),
        axis.text.x = element_text(size=8),
        axis.title = element_text(size=14),
        strip.text = element_text(size=10)) +
   geom_text(data = data_text %>% filter(Periode == Periode), 
             mapping = aes(x = x, y = y, label = label),
             size = 4, fontface = "bold")

5.6 Modèles Interactions

5.6.1 LM3_Int : N_Int & qtté fleurs

Interactions_Gestion %>% ggplot(aes (x = Qtte_Plantes, y = N_Interactions)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
  labs(x = "Quantité de plantes", y = "Nombre d'interactions") +
Interactions_Gestion %>% ggplot(aes (x = Temperature, y = N_Interactions)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
  labs(x = "Température (°C)", y = "Nombre d'interactions") +
Interactions_Gestion %>% ggplot(aes (x = Periode, y = N_Interactions)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) +
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) + 
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "a", y = 25, x = 1)) +
  geom_text(aes(label = "b", y = 26.5, x = 2)) +
  geom_text(aes(label = "b", y = 28, x = 3)) +
    labs(x = "Période", y = "Nombre d'interactions")

Interactions_Gestion %>% ggplot(aes (x = Qtte_Plantes, y = N_Interactions)) + 
  geom_point(aes (color = Gestion_moment_5)) + 
  geom_smooth(aes (color = Gestion_moment_5), method = "lm", se = T) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) + 
  theme(legend.position = "bottom",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    labs(x = "Quantité de plantes", y = "Nombre d'interactions", color = "Type de gestion")

data_text <- data.frame(label = c("a", "b", "ab", 
                                  "a", "b", "ab",
                                  "a", "a", "a",
                                  "a", "a", "a",
                                  "a", "b", "ab"),
                        Gestion_moment_5 = rep(c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"), each = 3),
                        x = c(1,2,3,
                              1,2,3,
                              1,2,3,
                              1,2,3,
                              1,2,3),
                        y = c(8,8,8,
                              10,18,7,
                              11,13,14,
                              4,11,9,
                              25,27,28))
data_text$Gestion_moment_5 <- fct_relevel(data_text$Gestion_moment_5, c("Graminées", "Fleuri", "Semé", "Tonte récente", "Tonte tardive"))
Interactions_Gestion %>% ggplot(aes (x = Periode, y = N_Interactions)) +
  facet_wrap(~Gestion_moment_5) +
 geom_boxplot(aes (color = Periode), alpha = 0.70) +
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) + 
  labs(x = "Période", y = "Nombre d'interactions", color = "Type de gestion") +
  theme(legend.position = "none",
        legend.title = element_text(size=11), 
        legend.text = element_text(size=10),
        axis.text.y = element_text(size=10),
        axis.text.x = element_text(size=8),
        axis.title = element_text(size=14),
        strip.text = element_text(size=10)) +
   geom_text(data = data_text %>% filter(Gestion_moment_5 == Gestion_moment_5), 
            mapping = aes(x = x, y = y, label = label),
            size = 4, fontface = "bold")

5.6.2 LM4_Int : sum(N_Int)

Température: voir LM3

Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5, Temperature) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Temperature, y = n)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  labs(x = "Température (°C)", y="Somme des interactions") +
  theme(axis.text=element_text(size=10),
        axis.title=element_text(size=14))

Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Periode, y = n)) + 
  geom_boxplot(aes (color = Periode), alpha = 0.70) + 
  scale_color_manual(values = c(Juin = "#74a9cf",
                                `Mi-juillet` = "#2b8cbe",
                                `Fin juillet` = "#045a8d")) +
  labs(x = "Période", y = "Somme des interactions", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "a", y = 137, x = 1)) +
  geom_text(aes(label = "a", y = 155, x = 2)) +
  geom_text(aes(label = "a", y = 75, x = 3)) +
Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Gestion_moment_5, y = n)) + 
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Somme des interactions", color = "Type de gestion") +
  theme(legend.position = "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14)) +
    geom_text(aes(label = "ab", y = 27, x = 1)) +
  geom_text(aes(label = "b", y = 91, x = 2)) +
  geom_text(aes(label = "c", y = 198, x = 3)) +
  geom_text(aes(label = "a", y = 48, x = 4)) +
  geom_text(aes(label = "b", y = 140, x = 5))

data_text <- data.frame(label = c("a", "b", "b","a", "b", 
                                  "a", "b", "c","a", "b",
                                  "ab", "ab", "b","a", "a"),
                        Periode = rep(c("Juin", "Mi-juillet", "Fin juillet"), each = 5),
                        x = c(1,2,3,4,5,
                              1,2,3,4,5,
                              1,2,3,4,5),
                        y = c(15,93,85,28,123,
                              25,80,198,10,113,
                              98,43,125,19,50))
data_text$Periode <- fct_relevel(data_text$Periode, c("Juin", "Mi-juillet", "Fin juillet"))
Interactions_Gestion %>% 
  group_by(Site_gestion_date, Periode, Gestion_moment_5) %>% 
  summarize(n= sum(N_Interactions)) %>% 
  ggplot(aes (x = Gestion_moment_5, y = n)) + 
  facet_wrap(~Periode)+
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
  scale_color_manual(values = c("Graminées" = "#fbcb09",
                                "Fleuri" = "#ff7207",
                                "Semé" = "#de1e21",
                                "Tonte récente" = "#6abe1d",
                                "Tonte tardive" = "#2b790c")) + 
  labs(x = "Période", y = "Somme des interactions", color = "Type de gestion") +
  theme(legend.position = "bottom",
        legend.title = element_text(size=11), 
        legend.text = element_text(size=10),
        axis.text.y = element_text(size=10),
        axis.text.x = element_text(size=8),
        axis.title = element_text(size=14),
        strip.text = element_text(size=10)) +
   geom_text(data = data_text %>% filter(Periode == Periode), 
             mapping = aes(x = x, y = y, label = label),
             size = 4, fontface = "bold")

5.6.3 LM5_nvis : nvis_fl_h

voir mod précédent effet t°, Perriode

Interactions_nvis_fl_h %>% 
  ggplot(aes (x = Periode, y = nvis_fl_h)) + 
  facet_wrap(~Sp_Pollinisateurs) +
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) +
    scale_y_continuous(trans='log10',
                     breaks=c(0.010, 0.100,1.000, 10),
                     labels=c("0.01","0.1","1", "10")) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(x = "Espèces de pollinisateurs", y = "Nombre de visites par fleur par heure (nvis/fl.h)", color= "Type de gestion") +
     theme(legend.position = "bottom",
        legend.title = element_text(size=11), 
        legend.text = element_text(size=10),
        axis.text = element_text(size=11),
        axis.title = element_text(size=14),
        strip.text = element_text(size=10, face = "italic"))

5.7 Interactions ou non

Interactions_Gestion %>% 
  group_by(Gestion_moment_5, Inter_YN) %>% 
  mutate(Inter_YN = as.factor(Inter_YN)) %>%
  summarize(n = n()) %>% 
  ggplot(aes(x = Gestion_moment_5, y = n)) +
  facet_wrap(~Inter_YN) +
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) +
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
   labs(x = "Type de gestion", y = "Compte") +
  theme(legend.position= "none",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14),
        strip.text = element_text(size = 12))

5.8 Nombre de changements de fleurs/sp

join_sum_n <- join_sum_n %>% 
  mutate(n = as.factor(n))
levels(join_sum_n$n) <- c("1 espèce", "2 espèces", "3 espèces")
join_sum_n %>% 
  ggplot(aes(x = Classe_Poll, y = sum))+
  facet_wrap(~n) +
  geom_boxplot(aes (color = Gestion_moment_5), alpha = 0.70) + 
    scale_color_manual(values = c("Graminées" = "#fbcb09",
                                  "Fleuri" = "#ff7207",
                                  "Semé" = "#de1e21",
                                  "Tonte récente" = "#6abe1d",
                                  "Tonte tardive" = "#2b790c")) +
  labs(x = "Classes de pollinisateurs", y = "Nombre de fleurs visitées en séquence", color = "Type de gestion", title = "Nombre d'espèces de plante différentes") + 
  theme(legend.position = "bottom", 
        axis.text.x = element_text(size = 6),
        axis.text.y=element_text(size=10),
        axis.title=element_text(size=14),
         strip.text = element_text(size = 12))

5.9 Réseaux Plantes_pollinisateurs

plotweb(network_fauche, high.lablength= 35, low.lablength=27, col.high=c("#aa1e0f"),col.low=c("#aa1e0f"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_tonte, high.lablength= 35, low.lablength=27, col.high=c("#12661f"),col.low=c("#12661f"),text.rot=90,y.width.high=0.07, y.width.low=0.07,y.lim=c(-1,3.5))

plotweb(network_gram, high.lablength= 35, low.lablength=27,  col.high=c("#fbcb09"),col.low=c("#fbcb09"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_fleuri, high.lablength= 35, low.lablength=27,  col.high=c("#ff7207"),col.low=c("#ff7207"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_seme, high.lablength= 35, low.lablength=27,  col.high=c("#de1e21"),col.low=c("#de1e21"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_TonRec, high.lablength= 35, low.lablength=27,  col.high=c("#6abe1d"), col.low=c("#6abe1d"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_TonTard, high.lablength= 35, low.lablength=27,  col.high=c("#2b790c"), col.low=c("#2b790c"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_Juin, high.lablength= 35, low.lablength=27,  col.high=c("#74a9cf"), col.low=c("#74a9cf"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_miJuil, high.lablength= 35, low.lablength=27,  col.high=c("#2b8cbe"), col.low=c("#2b8cbe"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

plotweb(network_finJuil, high.lablength= 35, low.lablength=27,  col.high=c("#045a8d"), col.low=c("#045a8d"), text.rot=90, y.width.high=0.07, y.width.low=0.07, y.lim=c(-1,3.5))

Table_NW_P %>%
  kbl(caption = "Période") %>%
  kable_classic(full_width = F, html_font = "Cambria") #%>% kable_styling() %>%
Période
Juin Mi-juillet Fin juillet
connectance 0.1352785 0.1303030 0.1064815
web asymmetry 0.3809524 0.3924051 0.3846154
nestedness 10.1552918 8.9542173 8.7682013
linkage density 4.7004410 4.2604178 3.6859752
Fisher alpha 61.9488092 44.7255342 40.7488484
Shannon diversity 3.7803507 3.5367435 3.1842605
interaction evenness 0.5165444 0.4922134 0.4442924
H2 0.5157954 0.4666628 0.5867317
robustness.HL 0.8013260 0.7962863 0.7359547
robustness.LL 0.6907693 0.6650232 0.6303276
generality.HL 3.9909709 3.5874657 3.0955332
vulnerability.LL 5.4099111 4.9333699 4.2764171
#save_kable(file = "Output/Tableau/NW_P.pdf")
Table_NW_G2 %>% 
  kbl(caption = "Gestion - biclassification") %>%
  kable_classic(full_width = F, html_font = "Cambria") # %>% kable_styling() %>%
Gestion - biclassification
Fauche Tonte
connectance 0.1181478 0.2943723
web asymmetry 0.4590164 0.6500000
nestedness 5.7725375 21.9889323
linkage density 6.9668799 2.1539505
Fisher alpha 102.2384405 13.8299152
Shannon diversity 4.5609652 1.6289212
interaction evenness 0.5711813 0.2993010
H2 0.4291655 0.6866345
robustness.HL 0.8092341 0.8664278
robustness.LL 0.6852618 0.6251094
generality.HL 5.2007056 1.3405415
vulnerability.LL 8.7330541 2.9673596
 # save_kable(file = "Output/Tableau/NW_G2.pdf")
Table_NW_G5 %>%
  kbl(caption = "Gestion - pentaclassification") %>%
  kable_classic(full_width = F, html_font = "Cambria") #%>% kable_styling() %>%
Gestion - pentaclassification
Graminées Fleuri Semé Tonte récente Tonte tardive
connectance 0.2407407 0.1142534 0.1418764 0.4722222 0.2857143
web asymmetry 0.6363636 0.4468085 0.4250000 0.6000000 0.6500000
nestedness 33.9172270 8.5064130 9.7964195 20.3768787 23.5572002
linkage density 4.5620168 5.5351438 4.9818744 1.9228986 2.1270847
Fisher alpha 15.7290348 71.1656509 54.1504976 4.6059563 13.6693767
Shannon diversity 3.2435071 4.2667950 3.8437780 1.2878123 1.6404026
interaction evenness 0.6375323 0.5706099 0.5354536 0.3593709 0.3014106
H2 0.7336450 0.5114385 0.4496612 0.8104420 0.6990216
robustness.HL 0.8496203 0.7821565 0.7777715 0.7576407 0.8713629
robustness.LL 0.5716149 0.6460864 0.6755235 0.5618750 0.6209114
generality.HL 1.4745200 3.8796971 3.3251203 1.1012144 1.3308515
vulnerability.LL 7.6495137 7.1905906 6.6386285 2.7445828 2.9233178
# save_kable(file = "Output/Tableau/NW_G5.pdf")

# write.csv2(Table_NW_G5, file = "Output/Tableau/NW_G5.csv")
# write.csv2(Table_NW_G2, file = "Output/Tableau/NW_G2.csv")
# write.csv2(Table_NW_P, file = "Output/Tableau/NW_P.csv")

5.10 Tonte

5.10.1 LM6_Tonte_S

Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = S, color = Site)) + 
  geom_point() +
  geom_smooth(se = F)+
  scale_x_continuous(n.breaks=17)+
  labs(x = "Jour après la tonte", y = "Richesse spécifique en plantes") +
        theme(legend.position = "bottom",
        axis.text=element_text(size=10),
        axis.title=element_text(size=14))

5.10.2 LM6_Tonte_Ab

Tonte_pl %>% ggplot(aes (x = Jour_af_tonte, y = Qtté, color=Espèces)) + 
  facet_wrap(~Site) +
  geom_smooth(se = F)+
  scale_x_continuous(n.breaks=17)+
  labs(x = "Jour après la tonte", y = "Abondance en plantes") +
           theme(legend.position = "bottom", 
        axis.text=element_text(size=10),
        axis.title=element_text(size=14),
         strip.text = element_text(size = 12))